forked from avehtari/BDA_course_Aalto
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgsu2021.html
991 lines (901 loc) · 49.7 KB
/
gsu2021.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<title>Bayesian Data Analysis course - GSU 2021</title>
<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/readable.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script src="site_libs/jqueryui-1.11.4/jquery-ui.min.js"></script>
<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />
<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
details > summary > p:only-child {
display: inline;
}
pre code {
padding: 0;
}
</style>
<style type="text/css">
.dropdown-submenu {
position: relative;
}
.dropdown-submenu>.dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
border-radius: 0 6px 6px 6px;
}
.dropdown-submenu:hover>.dropdown-menu {
display: block;
}
.dropdown-submenu>a:after {
display: block;
content: " ";
float: right;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
border-width: 5px 0 5px 5px;
border-left-color: #cccccc;
margin-top: 5px;
margin-right: -10px;
}
.dropdown-submenu:hover>a:after {
border-left-color: #adb5bd;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left>.dropdown-menu {
left: -100%;
margin-left: 10px;
border-radius: 6px 0 6px 6px;
}
</style>
<script type="text/javascript">
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark it active
menuAnchor.tab('show');
// if it's got a parent navbar menu mark it active as well
menuAnchor.closest('li.dropdown').addClass('active');
// Navbar adjustments
var navHeight = $(".navbar").first().height() + 15;
var style = document.createElement('style');
var pt = "padding-top: " + navHeight + "px; ";
var mt = "margin-top: -" + navHeight + "px; ";
var css = "";
// offset scroll position for anchor links (for fixed navbar)
for (var i = 1; i <= 6; i++) {
css += ".section h" + i + "{ " + pt + mt + "}\n";
}
style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;
document.head.appendChild(style);
});
</script>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before {
content: "";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "";
border: none;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
<style type="text/css">
#TOC {
margin: 25px 0px 20px 0px;
}
@media (max-width: 768px) {
#TOC {
position: relative;
width: 100%;
}
}
@media print {
.toc-content {
/* see https://github.com/w3c/csswg-drafts/issues/4434 */
float: right;
}
}
.toc-content {
padding-left: 30px;
padding-right: 40px;
}
div.main-container {
max-width: 1200px;
}
div.tocify {
width: 20%;
max-width: 260px;
max-height: 85%;
}
@media (min-width: 768px) and (max-width: 991px) {
div.tocify {
width: 25%;
}
}
@media (max-width: 767px) {
div.tocify {
width: 100%;
max-width: none;
}
}
.tocify ul, .tocify li {
line-height: 20px;
}
.tocify-subheader .tocify-item {
font-size: 0.90em;
}
.tocify .list-group-item {
border-radius: 0px;
}
</style>
</head>
<body>
<div class="container-fluid main-container">
<!-- setup 3col/9col grid for toc_float and main content -->
<div class="row">
<div class="col-xs-12 col-sm-4 col-md-3">
<div id="TOC" class="tocify">
</div>
</div>
<div class="toc-content col-xs-12 col-sm-8 col-md-9">
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-bs-toggle="collapse" data-target="#navbar" data-bs-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">Bayesian Data Analysis course</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="index.html">Material</a>
</li>
<li>
<a href="Aalto2022.html">Aalto 2022</a>
</li>
<li>
<a href="assignments.html">Assignments</a>
</li>
<li>
<a href="project.html">Project</a>
</li>
<li>
<a href="demos.html">Demos</a>
</li>
<li>
<a href="FAQ.html">FAQ</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<div id="header">
<h1 class="title toc-ignore">Bayesian Data Analysis course - GSU 2021</h1>
<h4 class="date">Page updated: 2021-11-10</h4>
</div>
<p>Bayesian Data Analysis Global South (GSU) 2021</p>
<p>Lecturer: <a href="https://users.aalto.fi/~ave/index.html">Aki Vehtari</a></p>
<ol style="list-style-type: decimal">
<li>Max 300 students with priority for global south and other underrepresented groups (GSU).</li>
<li>From 4th March (first assignment deadline 12th March) to 28th May .</li>
<li>All the material (textbook, videos, assignments, extra reading material) are freely available (see below) so you can also self-study in your own pace.</li>
<li>The course is free (no cost) and possible to organize with help of volunteer TAs.</li>
<li>This BDA course instance is aimed to support learning with peer support. By following the videos and doing assignments at the same time with others, you can discuss the material in assignments in the course slack, there is peer-grading platform to get feedback about your assignment solutions, and voluntary TAs help answering questions. As everything is volunteer based we can’t guarantee quick responses, but at least you will get something more than when studying only by yourself.</li>
<li>This course is not the easiest Bayesian course available in internet, but it can be your first Bayesian course if your mathematical and programming skills are sufficient. See the prerequisites below. For easier material to start with see the end of Prerequisites section below.</li>
<li>You will not get a formal certificate for passing the course from Aalto University.</li>
<li>The communication happens in the course slack, please don’t email the lecturer or TAs. The slack link has been emailed to the accepted students.</li>
</ol>
<p>All the course material is available in a <a href="https://github.com/avehtari/BDA_course_Aalto">git repo</a> and via these pages are for easier navigation. All the material can be used in other courses. Text and videos licensed under CC-BY-NC 4.0. Code licensed under BSD-3.</p>
<div id="registration" class="section level2">
<h2>Registration</h2>
<p>There has been some dropouts and the registration has been re-opened <a href="https://forms.gle/Zax7A7yDrzV47tDd6">here</a>.</p>
</div>
<div id="ta-registration" class="section level2">
<h2>TA Registration</h2>
<p>We have some volunteer TAs already, but a a few more would be great. All TAs will get a personal certificate from the lecturer Aki Vehtari if they actively participate helping students, answering questions, and possibly organize some TA sessions. We assume to have enough TAs that no-one needs to take part every week of the course and you can drop out if other obligations require so. The lecturer will support TAs.</p>
<p>To register as volunteer TA fill in your information (email, country, prerequisites check, a brief comment on justification why you think you can be a TA) <a href="https://forms.gle/Ey7NVQQ2qnBKfkK6A">here</a>.</p>
</div>
<div id="book-bda3" class="section level2">
<h2>Book: BDA3</h2>
<div style="float:right;position: relative;">
<p><img src="bda_cover.png" /></p>
</div>
<p><a href="https://users.aalto.fi/~ave/BDA3.pdf">The electronic version of the course book Bayesian Data Analysis, 3rd ed, by by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin</a> is available for non-commercial purposes. Hard copies are available from <a href="https://www.crcpress.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955">the publisher</a> and many book stores. See also <a href="http://www.stat.columbia.edu/~gelman/book/">home page for the book</a>, <a href="http://www.stat.columbia.edu/~gelman/book/errata_bda3.txt">errata for the book</a>, and <a href="chapter_notes/BDA_notes.pdf">chapter notes</a>.</p>
</div>
<div id="prerequisites" class="section level2">
<h2>Prerequisites</h2>
<ul>
<li>Basic terms of probability theory
<ul>
<li>probability, probability density, distribution</li>
<li>sum, product rule, and Bayes’ rule</li>
<li>expectation, mean, variance, median</li>
<li>in English, see e.g. Wikipedia and <a href="https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/">Introduction to probability and statistics</a></li>
</ul></li>
<li>Some algebra and calculus</li>
<li>Basic visualisation techniques (R or Python)
<ul>
<li>histogram, density plot, scatter plot</li>
<li>see e.g. <a href="demos.html#BDA_R_demos">BDA R demos</a></li>
<li>see e.g. <a href="demos.html#BDA_Python_demos">BDA Python demos</a></li>
</ul></li>
</ul>
<p>This course has been designed so that there is strong emphasis in computational aspects of Bayesian data analysis and using the latest computational tools.</p>
<p>If you find BDA3 too difficult to start with, I recommend</p>
<ul>
<li>For regression models, their connection to statistical testing and causal analysis see <a href="https://avehtari.github.io/ROS-Examples/">Gelman, Hill and Vehtari, “Regression and Other Stories”</a>.</li>
<li>Richard McElreath’s <a href="https://xcelab.net/rm/statistical-rethinking/">Statistical Rethinking, 2nd ed</a> book is easier than BDA3 and the 2nd ed is excellent. Statistical Rethinking doesn’t go as deep in some details, math, algorithms and programming as BDA course. Richard’s lecture videos of <a href="https://github.com/rmcelreath/statrethinking_winter2019">Statistical Rethinking: A Bayesian Course Using R and Stan</a> are highly recommended even if you are following BDA3.</li>
<li>For background prerequisites some students have found chapters 2, 4 and 5 in <a href="https://sites.google.com/site/doingbayesiandataanalysis/">Kruschke, “Doing Bayesian Data Analysis”</a> useful.</li>
</ul>
</div>
<div id="communication-channels" class="section level2">
<h2>Communication channels</h2>
<ul>
<li>The primary communication channel is the course slack. The slack link has been emailed to the accepted students.
<ul>
<li>Don’t ask via email or direct messages. By asking via common channels in the course chat, more eyes will see your question, it will get answered faster and it’s likely that other students benefit from the answer.</li>
<li>In the chat system, there are separate channels for each assignment and the project.</li>
<li>Channel <strong>#general</strong> can be used for any kind of general discussions and questions related to the course.</li>
<li>All important announcements will be posted to <strong>#announcements</strong> (no discussion on this channel).</li>
<li>Any kind of feedback is welcome on channel <strong>#feedback</strong>.</li>
<li>We have also channels <strong>#r</strong>, <strong>#python</strong>, and <strong>#stan</strong> for questions that are not specific to assignments or the project.</li>
<li>The lecturer and teaching assistants have names with “(staff)” or “(TA)” in the end of their names.</li>
</ul></li>
<li>The lecturer will answer weekly the best questions about the material, either in text, recording additional video clips, or in live Q&A session (in this course instance no guarantee for weekly live session)</li>
<li>In this course instance all the TAs are volunteers from different time zones, but there may be possibility for live “TA sessions” with TAs depending on the volunteers</li>
<li>If you find errors in material, post in <strong>#feedback</strong> channel or <a href="https://github.com/avehtari/BDA_course_Aalto/issues">submit an issue in github</a>.</li>
<li>Peergrade alerts: If you are worried that you forget the deadlines, you can set peergade to send you email when assignment opens for submission, 24 hours before assignment close for submission, assignment is open for reviewing, 24 hours before an assignment closes for reviewing if you haven’t started yet, someone likes my feedback (once a day). Click your name -> User Settings to choose which alerts you want.</li>
</ul>
</div>
<div id="assessment" class="section level2">
<h2>Assessment</h2>
<p><a href="assignments_gsu.html">Assignments</a> (67%) and a <a href="project_gsu.html">project work with presentation</a> (33%). Minimum of 50% of points must be obtained from both the assignments and project work. But as in this course there is no formal certificate, the assignment scores are just for your own self-evaluation. The biggest benefit from the course is the support and feedback from other students and volunteer TAs.</p>
<p>We use <a href="https://peergrade.io/">peergrade.io</a> for providing peer feedback for the assignments and the project work. See more information at <a href="assignments_gsu.html">Assignments</a>.</p>
</div>
<div id="schedule-2021" class="section level2">
<h2>Schedule 2021</h2>
<p>The course consists of 12 blocks from March to May 2021. The blocks don’t match exactly specific weeks. For example, it’s good start reading the material for the next block while making the assignment for one block. There are 9 assignments and a project work with presentation, and thus the assignments are not in one-to-one correspondence with the blocks. The schedule below lists the blocks and how they connect to the topics, book chapters and assignments.</p>
<div id="schedule-overview" class="section level3">
<h3>Schedule overview</h3>
<p>Here is an overview of the schedule. Scroll down the page to see detailed instructions for each block. Remember that blocks are overlapping so that when you are working on assignment for one block, you should start watching videos and reading text for the next block.</p>
<table>
<colgroup>
<col width="0%" />
<col width="4%" />
<col width="26%" />
<col width="54%" />
<col width="9%" />
<col width="3%" />
<col width="1%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th align="left">Block</th>
<th>Readings</th>
<th>Lectures</th>
<th>Assignment</th>
<th></th>
<th>Assignment due date</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td></td>
<td align="left">1. Introduction</td>
<td><a href="chapter_notes/BDA_notes_ch1.pdf">BDA3 Chapter 1</a></td>
<td><a href="https://www.youtube.com/watch?v=ukE5aqdoLZI">Computational probabilistic modeling</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d841f429-9c3d-4d24-8228-a9f400efda7b">Introduction to uncertainty and modelling</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=13fc7889-cfd1-4d99-996c-a9f400f6e5a2">Introduction to the course contents</a></td>
<td><a href="assignments/assignment1.pdf">Assignment 1</a>, <br> <a href="assignments/assignment1_rubric.html">Rubric questions</a></td>
<td></td>
<td>14 March</td>
</tr>
<tr class="even">
<td></td>
<td align="left">2. Basics of Bayesian inference</td>
<td><a href="chapter_notes/BDA_notes_ch1.pdf">BDA3 Chapter 1</a>, <br> <a href="chapter_notes/BDA_notes_ch2.pdf">BDA3 Chapter 2</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=9c271082-5a8c-4b66-b6c2-aacc00fc683f">Lecture 2.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=70655a8a-0eb4-4ddd-9f52-aacc00fc67a2">Lecture 2.2</a>, <br> Optional: <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=158d119d-8673-4120-8669-ac3900c13304">Extra explanations 2</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=7a297f7d-bb7b-4dd0-9913-a9f500ec822d">Summary 2.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=75b9f18f-e379-4557-a5fa-a9f500f11b40">Summary 2.2</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=099659a5-f707-473d-8b03-a9f500f39eb5">Summary 2.3</a></td>
<td><a href="assignments/assignment2.pdf">Assignment 2</a>, <br> <a href="assignments/assignment2_rubric.html">Rubric questions</a></td>
<td></td>
<td>19.03.</td>
</tr>
<tr class="odd">
<td></td>
<td align="left">3. Multidimensional posterior</td>
<td><a href="chapter_notes/BDA_notes_ch3.pdf">BDA3 Chapter 3</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ab958b4b-e2c4-4534-8305-aad100ba191f">Lecture 3</a></td>
<td><a href="assignments/assignment3.pdf">Assignment 3</a>, <br> <a href="assignments/assignment3_rubric.html">Rubric questions</a></td>
<td></td>
<td>26.03.</td>
</tr>
<tr class="even">
<td></td>
<td align="left">4. Monte Carlo</td>
<td><a href="chapter_notes/BDA_notes_ch10.pdf">BDA3 Chapter 10</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=8a3c7bbc-e2b8-4c16-97b2-aad800ba7927">Lecture 4.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=44446861-eaa2-41b5-bf33-aad800caf18a">Lecture 4.2</a></td>
<td><a href="assignments/assignment4.pdf">Assignment 4</a>, <br> <a href="assignments/assignment4_rubric.html">Rubric questions</a></td>
<td></td>
<td>09.04.</td>
</tr>
<tr class="odd">
<td></td>
<td align="left">5. Markov chain Monte Carlo</td>
<td><a href="chapter_notes/BDA_notes_ch11.pdf">BDA3 Chapter 11</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=098dfdb4-f3b8-46aa-b988-aadf00bd3177">Lecture 5.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=9f657178-d8cf-4cb8-af62-aadf00cd9423">Lecture 5.2</a></td>
<td><a href="assignments/assignment5.pdf">Assignment 5</a>, <br> <a href="assignments/assignment5_rubric.html">Rubric questions</a></td>
<td></td>
<td>16.04.</td>
</tr>
<tr class="even">
<td></td>
<td align="left">6. Stan, HMC, PPL</td>
<td><a href="chapter_notes/BDA_notes_ch12.pdf">BDA3 Chapter 12</a> + <a href="index.html#stan">extra material on Stan</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=1744f6a0-84d3-4218-8a86-aae600ba7e84">Lecture 6.1</a>, <br><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e60ba1a9-f752-4b0a-88c6-aae600caa61a">Lecture 6.2</a></td>
<td><a href="assignments/assignment6.pdf">Assignment 6</a>, <br> <a href="assignments/assignment6_rubric.html">Rubric questions</a></td>
<td></td>
<td>23.04.</td>
</tr>
<tr class="odd">
<td></td>
<td align="left">7. Hierarchical models and exchangeability</td>
<td><a href="chapter_notes/BDA_notes_ch5.pdf">BDA3 Chapter 5</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=79dee6de-afa9-446f-b533-aaf400cabf2b">Lecture 7.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c822561c-f95d-44fc-a1d0-aaf400d9fae3">Lecture 7.2</a></td>
<td><a href="assignments/assignment7.pdf">Assignment 7</a>, <br> <a href="assignments/assignment7_rubric.html">Rubric questions</a></td>
<td></td>
<td>30.04.</td>
</tr>
<tr class="even">
<td></td>
<td align="left">8. Model checking & cross-validation</td>
<td><a href="chapter_notes/BDA_notes_ch6.pdf">BDA3 Chapter 6</a>, <a href="chapter_notes/BDA_notes_ch7.pdf">BDA3 Chapter 7</a>, <a href="https://doi.org/10.1111/rssa.12378">Visualization in Bayesian workflow</a>, <a href="https://arxiv.org/abs/1507.04544">Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=7047e366-0df6-453c-867f-aafb00ca2d78">Lecture 8.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d7849131-0afd-4ae6-ad64-aafb00da36f4">Lecture 8.2</a></td>
<td>Start project work</td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td align="left">9. Model comparison and selection</td>
<td><a href="chapter_notes/BDA_notes_ch7.pdf">BDA3 Chapter 7 (not 7.2 and 7.3)</a>, <br> <a href="https://arxiv.org/abs/1507.04544">Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=50b2e73f-af0a-4715-b627-ab0200ca7bbd">Lecture 9.1</a>, <br> Optional: <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=b0299d53-9454-4e33-9086-ab0200db14ee">Lecture 9.2</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=4b6eeb48-ae64-4860-a8c3-ab0200e40ad8">Lecture 9.3</a></td>
<td><a href="assignments/assignment8.pdf">Assignment 8</a>, <br> <a href="assignments/assignment8_rubric.html">Rubric questions</a></td>
<td></td>
<td>07.05.</td>
</tr>
<tr class="even">
<td></td>
<td align="left">10. Decision analysis</td>
<td><a href="chapter_notes/BDA_notes_ch9.pdf">BDA3 Chapter 9</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=82943720-de0f-4195-8639-ab0900ca2085">Lecture 10.1</a></td>
<td><a href="assignments/assignment9.pdf">Assignment 9</a>, <br> <a href="assignments/assignment9_rubric.html">Rubric questions</a></td>
<td></td>
<td>14.05.</td>
</tr>
<tr class="odd">
<td></td>
<td align="left">11. Normal approximation, frequency properties</td>
<td><a href="chapter_notes/BDA_notes_ch4.pdf">BDA3 Chapter 4</a></td>
<td><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e22fedc7-9fd3-4d1e-8318-ab1000ca45a4">Lecture 11.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=a8e38a95-a944-4f3d-bf95-ab1000dbdf73">Lecture 11.2</a></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td align="left">12. Extended topics</td>
<td>Optional: BDA3 Chapter 8, <br> BDA3 Chapter 14-18, <br> BDA3 Chapter 21</td>
<td>Optional: <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e998b5dd-bf8e-42da-9f7c-ab1700ca2702">Lecture 12.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c43c862a-a5a4-45da-9b27-ab1700e12012">Lecture 12.2</a></td>
<td>Project work</td>
<td></td>
<td>21.05.</td>
</tr>
<tr class="odd">
<td></td>
<td align="left">13. Project feedback</td>
<td></td>
<td></td>
<td>Project feedback</td>
<td></td>
<td>28.05.</td>
</tr>
</tbody>
</table>
</div>
<div id="course-introduction-bda-3-ch-1-prerequisites-assignment" class="section level3">
<h3>1) Course introduction, BDA 3 Ch 1, prerequisites assignment</h3>
<p>Course practicalities, material, assignments, project work, peergrading, TA sessions, prerequisites, chat, etc.</p>
<ul>
<li>Login to the course chat (link will be emailed to the registered students and volunteer TAs)</li>
<li>Signin to <a href="https://www.peergrade.io">Peergrade</a> with the class code shared in email</li>
<li>Watch videos
<ul>
<li><a href="https://www.youtube.com/watch?v=ukE5aqdoLZI">Computational probabilistic modeling</a> (15min)</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d841f429-9c3d-4d24-8228-a9f400efda7b">Introduction to uncertainty and modelling</a> (18min)</li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=13fc7889-cfd1-4d99-996c-a9f400f6e5a2">Introduction to the course contents</a> (8min)</li>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a> /- Read BDA3 Chapter 1</li>
<li>start with <a href="chapter_notes/BDA_notes_ch1.pdf">reading instructions for Chapter 1</a> and afterwards read the additional comments in the same document</li>
</ul></li>
<li>There are no R/Python demos for Chapter 1</li>
<li>Make and submit <a href="assignments/assignment1.pdf">Assignment 1</a>. <strong>Deadline Friday 14 March 23:59 UTC+2</strong>
<ul>
<li>this assignment checks that you have sufficient prerequisite skills (basic probability calculus, and R or Python)
<ul>
<li><a href="assignments/assignment1_rubric.html">Rubric questions used in peergrading for Assignment 1</a></li>
</ul></li>
<li><a href="assignments_gsu.html">General information about assignments</a>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/templates/">R markdown template for assignments</a></li>
<li><a href="FAQ.html">FAQ for the assignments</a> has solutions to commonly asked questions related RStudio setup, errors during package installations, etc.</li>
</ul></li>
</ul></li>
<li>Optional: Make BDA3 exercises 1.1-1.4, 1.6-1.8 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solutions available for 1.1-1.6</a>)</li>
<li>Start reading Chapters 1+2, see instructions below</li>
</ul>
</div>
<div id="bda3-ch-12-basics-of-bayesian-inference" class="section level3">
<h3>2) BDA3 Ch 1+2, basics of Bayesian inference</h3>
<p>BDA3 Chapters 1+2, basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model.</p>
<ul>
<li>Read BDA3 Chapter 2
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch2.pdf">reading instructions for Chapter 2</a></li>
</ul></li>
<li>Watch videos <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=9c271082-5a8c-4b66-b6c2-aacc00fc683f">Lecture 2.1</a> and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=70655a8a-0eb4-4ddd-9f52-aacc00fc67a2">Lecture 2.2</a> on basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model. BDA3 Ch 1+2.
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=158d119d-8673-4120-8669-ac3900c13304">Extra explanations about likelihood, normalization term, density, and conditioning on model M</a></li>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Optional summary videos:
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=7a297f7d-bb7b-4dd0-9913-a9f500ec822d">2.1 Observation model, likelihood, posterior and binomial model</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=75b9f18f-e379-4557-a5fa-a9f500f11b40">2.2 Predictive distribution and benefit of integration</a></li>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=099659a5-f707-473d-8b03-a9f500f39eb5">2.3 Priors and prior information</a></li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch2.pdf">the additional comments for Chapter 2</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> or <a href="demos.html#BDA_Python_demos">Python demos</a> for Chapter 2</li>
<li>Make and submit <a href="assignments/assignment2.pdf">Assignment 2</a>. <strong>Deadline Friday 19 March 23:59 UTC+2</strong>
<ul>
<li><a href="assignments/assignment2_rubric.html">Rubric questions used in peergrading for Assignment 2</a></li>
<li>Review Assignment 1 done by your peers before 23:59 UTC+2 17 March</li>
<li>Reflect on your feedback</li>
</ul></li>
<li>Optional: Make BDA3 exercises 2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solutions available for 2.1-2.5, 2.7-2.13, 2.16, 2.17, 2.20</a>, and 2.14 is in course slides)</li>
<li>Start reading Chapter 3, see instructions below</li>
</ul>
</div>
<div id="bda3-ch-3-multidimensional-posterior" class="section level3">
<h3>3) BDA3 Ch 3, multidimensional posterior</h3>
<p>Multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.</p>
<ul>
<li>Read BDA3 Chapter 3
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch3.pdf">reading instructions for Chapter 3</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ab958b4b-e2c4-4534-8305-aad100ba191f">Lecture 3</a> on multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch3.pdf">the additional comments for Chapter 3</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> or <a href="demos.html#BDA_Python_demos">Python demos</a> for Chapter 3</li>
<li>Make and submit <a href="assignments/assignment3.pdf">Assignment 3</a>. <strong>Deadline Friday 26 March 23:59 UTC+2</strong>
<ul>
<li><a href="assignments/assignment3_rubric.html">Rubric questions used in peergrading for Assignment 3</a></li>
<li>Review Assignment 2 done by your peers before 23:59 UTC+2 24 March, and reflect on your feedback</li>
</ul></li>
<li>Optional: Make BDA3 exercises 3.2, 3.3, 3.9 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solutions available for 3.1-3.3, 3.5, 3.9, 3.10</a>)</li>
<li>Start reading Chapter 10, see instructions below</li>
</ul>
</div>
<div id="bda3-ch-10-monte-carlo" class="section level3">
<h3>4) BDA3 Ch 10, Monte Carlo</h3>
<p>Numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.</p>
<ul>
<li>Read BDA3 Chapter 10
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch10.pdf">reading instructions for Chapter 10</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=8a3c7bbc-e2b8-4c16-97b2-aad800ba7927">Lecture 4.1</a> on numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=44446861-eaa2-41b5-bf33-aad800caf18a">Lecture 4.2</a> on direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch10.pdf">the additional comments for Chapter 10</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> or <a href="demos.html#BDA_Python_demos">Python demos</a> for Chapter 10</li>
<li>Make and submit <a href="assignments/assignment4.pdf">Assignment 4</a>. <strong>Deadline Friday 9 April 23:59 UTC+3</strong>
<ul>
<li><a href="assignments/assignment4_rubric.html">Rubric questions used in peergrading for Assignment 4</a></li>
<li>Review Assignment 3 done by your peers before 23:59 UTC+3 7 April, and reflect on your feedback</li>
</ul></li>
<li>Optional: Make BDA3 exercises 10.1, 10.2 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solution available for 10.4</a>)</li>
<li>Start reading Chapter 11, see instructions below</li>
</ul>
</div>
<div id="bda3-ch-11-markov-chain-monte-carlo" class="section level3">
<h3>5) BDA3 Ch 11, Markov chain Monte Carlo</h3>
<p>Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.</p>
<ul>
<li>Read BDA3 Chapter 11
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch11.pdf">reading instructions for Chapter 11</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=098dfdb4-f3b8-46aa-b988-aadf00bd3177">Lecture 5.1</a> on Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=9f657178-d8cf-4cb8-af62-aadf00cd9423">Lecture 5.2</a> on warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch11.pdf">the additional comments for Chapter 11</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> or <a href="demos.html#BDA_Python_demos">Python demos</a> for Chapter 11</li>
<li>Make and submit <a href="assignments/assignment5.pdf">Assignment 5</a>. <strong>Deadline Friday 16 April 23:59 UTC+3</strong>
<ul>
<li><a href="assignments/assignment5_rubric.html">Rubric questions used in peergrading for Assignment 5</a></li>
<li>Review Assignment 4 done by your peers before 23:59 UTC+3 14 April, and reflect on your feedback</li>
</ul></li>
<li>Optional: Make BDA3 exercise 11.1 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solution available for 11.1</a>)</li>
<li>Start reading Chapter 12 + Stan material, see instructions below</li>
</ul>
</div>
<div id="bda3-ch-12-stan-hmc-ppl-stan" class="section level3">
<h3>6) BDA3 Ch 12 + Stan, HMC, PPL, Stan</h3>
<p>HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, probabilistic programming and Stan. BDA3 Ch 12 + <a href="index.html#stan">extra material</a></p>
<ul>
<li>Read BDA3 Chapter 12
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch12.pdf">reading instructions for Chapter 12</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=1744f6a0-84d3-4218-8a86-aae600ba7e84">Lecture 6.1</a> on HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e60ba1a9-f752-4b0a-88c6-aae600caa61a">Lecture 6.2</a> on probabilistic programming and Stan. BDA3 Ch 12 + <a href="#stan">extra material</a>.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
<li>Optional: <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=f4b61f2a-4a94-43f7-828c-ac460144f64f">Stan Extra introduction recorded 2020</a> Golf putting example, main features of Stan, benefits of probabilistic programming, and comparison to some other software.</li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch12.pdf">the additional comments for Chapter 12</a></li>
<li>Read <a href="http://www.stat.columbia.edu/~gelman/research/published/Stan-paper-aug-2015.pdf">Stan introduction article</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> for RStan or <a href="demos.html#BDA_Python_demos">Python demos</a> for PyStan</li>
<li>Additional material for Stan:
<ul>
<li><a href="http://mc-stan.org/documentation/">Documentation</a></li>
<li><a href="https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started">RStan installation</a></li>
<li><a href="https://pystan.readthedocs.io/en/latest/getting_started.html">PyStan installation</a></li>
<li>Basics of Bayesian inference and Stan, Jonah Gabry & Lauren Kennedy <a href="https://www.youtube.com/watch?v=ZRpo41l02KQ&t=8s&list=PLuwyh42iHquU4hUBQs20hkBsKSMrp6H0J&index=6">Part 1</a> and <a href="https://www.youtube.com/watch?v=6cc4N1vT8pk&t=0s&list=PLuwyh42iHquU4hUBQs20hkBsKSMrp6H0J&index=7">Part 2</a></li>
</ul></li>
<li>Make and submit <a href="assignments/assignment6.pdf">Assignment 6</a>. <strong>Deadline Friday 23 April 23:59 UTC+3</strong>
<ul>
<li><a href="assignments/assignment6_rubric.html">Rubric questions used in peergrading for Assignment 6</a></li>
<li>Review Assignment 5 done by your peers before 23:59 UTC+3 21 April, and reflect on your feedback</li>
</ul></li>
<li>Start reading Chapter 5 + Stan material, see instructions below</li>
</ul>
</div>
<div id="bda3-ch-5-hierarchical-models" class="section level3">
<h3>7) BDA3 Ch 5, hierarchical models</h3>
<p>Hierarchical models and exchangeability. BDA3 Ch 5.</p>
<ul>
<li>Read BDA3 Chapter 5
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch5.pdf">reading instructions for Chapter 5</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=79dee6de-afa9-446f-b533-aaf400cabf2b">Lecture 7.1</a> on hierarchical models, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c822561c-f95d-44fc-a1d0-aaf400d9fae3">Lecture 7.2</a> on exchangeability. BDA3 Ch 5.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch5.pdf">the additional comments for Chapter 5</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> or <a href="demos.html#BDA_Python_demos">Python demos</a> for Chapter 5</li>
<li>Make and submit <a href="assignments/assignment7.pdf">Assignment 7</a>. <strong>Deadline Friday 30 April 23:59 UTC+3</strong>
<ul>
<li><a href="assignments/assignment7_rubric.html">Rubric questions used in peergrading for Assignment 7</a></li>
<li>Review Assignment 6 done by your peers before 23:59 UTC+3 28 April, and reflect on your feedback</li>
</ul></li>
<li>Optional: Make BDA3 exercises 5.1 and 5.1 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solution available for 5.3-5.5, 5.7-5.12</a>)</li>
<li>Start reading Chapters 6-7 and additional material, see instructions below.</li>
</ul>
</div>
<div id="bda3-ch-67-extra-material-model-checking-cross-validation" class="section level3">
<h3>8) BDA3 Ch 6+7 + extra material, model checking, cross-validation</h3>
<p>Model checking and cross-validation.</p>
<ul>
<li>Read BDA3 Chapters 6 and 7 (skip 7.2 and 7.3)
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch6.pdf">reading instructions for Chapter 6</a> and <a href="chapter_notes/BDA_notes_ch7.pdf">Chapter 7</a></li>
</ul></li>
<li>Read <a href="https://doi.org/10.1111/rssa.12378">Visualization in Bayesian workflow</a>
<ul>
<li>more about workflow and examples of prior predictive checking and LOO-CV probability integral transformations</li>
</ul></li>
<li>Read <a href="https://arxiv.org/abs/1507.04544">Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC</a> (<a href="https://doi.org/10.1007/s11222-016-9696-4">Journal link</a>)
<ul>
<li>replaces BDA3 Sections 7.2 and 7.3 on cross-validation</li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=7047e366-0df6-453c-867f-aafb00ca2d78">Lecture 8.1</a> on model checking, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d7849131-0afd-4ae6-ad64-aafb00da36f4">Lecture 8.2</a> on cross-validation part 1. BDA3 Ch 6-7 + extra material.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Read <a href="chapter_notes/BDA_notes_ch6.pdf">the additional comments for Chapter 6</a> and <a href="chapter_notes/BDA_notes_ch7.pdf">Chapter 7</a></li>
<li>Check <a href="demos.html#BDA_R_demos">R demos</a> or <a href="demos.html#BDA_Python_demos">Python demos</a> for Chapter 6</li>
<li>Additional reading material
<ul>
<li><a href="https://avehtari.github.io/modelselection/">Model selection</a></li>
<li><a href="https://avehtari.github.io/modelselection/CV-FAQ.html">Cross-validation FAQ</a></li>
</ul></li>
<li>No new assignment in this block</li>
<li>Start <a href="project.Rmd">the project work</a></li>
<li>Optional: Make BDA3 exercise 6.1 (<a href="http://www.stat.columbia.edu/~gelman/book/solutions3.pdf">model solution available for 5.3-5.5, 5.7-5.12</a>)</li>
</ul>
</div>
<div id="bda3-ch-7-extra-material-model-comparison-and-selection" class="section level3">
<h3>9) BDA3 Ch 7, extra material, model comparison and selection</h3>
<p>PSIS-LOO, K-fold-CV, model comparison and selection. Extra lecture on variable selection with projection predictive variable selection.</p>
<ul>
<li>Read Chapter 7 (no 7.2 and 7.3)
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch7.pdf">reading instructions for Chapter 7</a></li>
</ul></li>
<li>Read <a href="https://arxiv.org/abs/1507.04544">Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC</a> (<a href="https://doi.org/10.1007/s11222-016-9696-4">Journal link</a>)
<ul>
<li>replaces BDA3 Sections 7.2 and 7.3 on cross-validation</li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=50b2e73f-af0a-4715-b627-ab0200ca7bbd">Lecture 9.1</a> PSIS-LOO and K-fold-CV.</li>
<li>Optional: <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=b0299d53-9454-4e33-9086-ab0200db14ee">Lecture 9.2</a> model comparison and selection, and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=4b6eeb48-ae64-4860-a8c3-ab0200e40ad8">Lecture 9.3</a> extra lecture on variable selection with projection predictive variable selection. Extra material.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Additional reading material
<ul>
<li><a href="https://avehtari.github.io/modelselection/">Model selection</a></li>
<li><a href="https://avehtari.github.io/modelselection/CV-FAQ.html">Cross-validation FAQ</a></li>
</ul></li>
<li>Make and submit <a href="assignments/assignment8.pdf">Assignment 8</a>. <strong>Deadline Friday 7 May 23:59 UTC+3</strong>
<ul>
<li><a href="assignments/assignment8_rubric.html">Rubric questions used in peergrading for Assignment 8</a></li>
<li>Review Assignment 7 done by your peers before 23:59 UTC+3 5 May, and reflect on your feedback</li>
</ul></li>
<li>Start reading Chapter 9, see instructions below.</li>
</ul>
</div>
<div id="bda3-ch-9-decision-analysis" class="section level3">
<h3>10) BDA3 Ch 9, decision analysis</h3>
<p>Decision analysis. BDA3 Ch 9.</p>
<ul>
<li>Read Chapter 9
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch9.pdf">reading instructions for Chapter 9</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=82943720-de0f-4195-8639-ab0900ca2085">Lecture 10.1</a> on decision analysis. BDA3 Ch 9.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Project presentation info will be updated soon.</li>
<li>Make and submit <a href="assignments/assignment9.pdf">Assignment 9</a>. <strong>Deadline Friday 14 May 23:59 UTC+3</strong>
<ul>
<li><a href="assignments/assignment9_rubric.html">Rubric questions used in peergrading for Assignment 9</a></li>
<li>Review Assignment 8 done by your peers before 23:59 UTC+3 12 May, and reflect on your feedback</li>
</ul></li>
<li>Start reading Chapter 4, see instructions below.</li>
</ul>
</div>
<div id="bda3-ch-4-extra-material-normal-approximation-frequency-properties" class="section level3">
<h3>11) BDA3 Ch 4 + extra material, normal approximation, frequency properties</h3>
<p>Normal approximation (Laplace approximation), and large sample theory and counter examples. BDA3 Ch 4.</p>
<ul>
<li>Read Chapter 4
<ul>
<li>see <a href="chapter_notes/BDA_notes_ch4.pdf">reading instructions for Chapter 4</a></li>
</ul></li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e22fedc7-9fd3-4d1e-8318-ab1000ca45a4">Lecture 11.1</a> on normal approximation (Laplace approximation) and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=a8e38a95-a944-4f3d-bf95-ab1000dbdf73">Lecture 11.2</a> on large sample theory and counter examples. BDA3 Ch 4.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>No new assignment. Work on project.
<ul>
<li>Review Assignment 9 done by your peers before 23:59 UTC+3 19 May, and reflect on your feedback</li>
</ul></li>
</ul>
</div>
<div id="extra-material-overview-of-bda3-ch-8-14-18-21" class="section level3">
<h3>12) extra material + overview of BDA3 Ch 8, 14-18, 21</h3>
<p>Frequency evaluation of Bayesian methods, hypothesis testing and variable selection. Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21.</p>
<ul>
<li>These lectures are optional, but especially the lecture on hypothesis testing and variable selection is useful for project work.</li>
<li>Watch <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e998b5dd-bf8e-42da-9f7c-ab1700ca2702">Lecture 12.1</a> on frequency evaluation, hypothesis testing and variable selection and <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c43c862a-a5a4-45da-9b27-ab1700e12012">Lecture 12.2</a> overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21.
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Work on project. TAs help with projects. <strong>Project deadline 21 May 23:59 UTC+3</strong></li>
</ul>
</div>
<div id="project-evaluation" class="section level3">
<h3>13) Project evaluation</h3>
<ul>
<li>Project report deadline 21 May 23:59 UTC+3 (submit to peergrade).
<ul>
<li>Review project reports done by your peers before 26 May 23:59 UTC+3, and reflect on your feedback</li>
</ul></li>
</ul>
</div>
</div>
<div id="r-and-python" class="section level2">
<h2>R and Python</h2>
<p>We strongly recommend using R in the course as there are more packages for Stan and statistical analysis in R. If you are already fluent in Python, but not in R, then using Python may be easier, but it can still be more useful to learn also R. Unless you are already experienced and have figured out your preferred way to work with R, we recommend</p>
<ul>
<li>installing <a href="https://www.rstudio.com/products/rstudio/download/">RStudio Desktop</a>,</li>
</ul>
<p>See <a href="FAQ.html">FAQ</a> for frequently asked questions about R problems in this course. The <a href="demos.html">demo codes</a> provide useful starting points for all the assignments.</p>
<ul>
<li>For learning R programming basics
<ul>
<li><a href="https://rstudio-education.github.io/hopr/">Garrett Grolemund, Hands-On Programming with R</a></li>
</ul></li>
<li>For learning basic and advanced plotting using R
<ul>
<li><a href="https://socviz.co/">Kieran Healy, Data Visualization - A practical introduction</a></li>
<li><a href="http://www.gradaanwr.net/">Antony Unwin, Graphical Data Analysis with R</a></li>
</ul></li>
</ul>
</div>
</div>
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<script>
$(document).ready(function () {
// temporarily add toc-ignore selector to headers for the consistency with Pandoc
$('.unlisted.unnumbered').addClass('toc-ignore')
// move toc-ignore selectors from section div to header
$('div.section.toc-ignore')
.removeClass('toc-ignore')
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
// establish options
var options = {
selectors: "h1,h2,h3",
theme: "bootstrap3",
context: '.toc-content',
hashGenerator: function (text) {
return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
},
ignoreSelector: ".toc-ignore",
scrollTo: 0
};
options.showAndHide = true;
options.smoothScroll = true;
// tocify
var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>