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<h1 class="title toc-ignore">Bayesian Data Analysis course - Aalto 2022</h1>
<h4 class="date">Page updated: 2022-10-05</h4>
</div>
<!--
**The first lecture is
5.9.2022 in hall A,
Otakaari 1**
-->
<p><strong>Aalto 2022</strong> course can be taken online except for the final project presentation. The lectures will be given on campus, but recorded and the recording will be made available online after the course. If you are unable to register for the course at the moment in the Sisu, there is no need to email the lecturer. You can start taking the course and register before the end of the course. Sisu shows rooms on campus for the computer exercises, but only some of the computer exercises and TA sessions are on campus and most of the session are online (we are preparing the schedule). You can choose which TA session to join each week separately, without a need to register for those sessions.</p>
<ul>
<li><a href="https://mycourses.aalto.fi/course/view.php?id=37048">MyCourses</a> is used for initial announcements, linking to peergrade, and some questionnaires.</li>
<li>Most of the communication happens in the course chat (see below)</li>
<li><a href="https://into.aalto.fi/display/ensaannot/Aalto+University+Code+of+Academic+Integrity+and+Handling+Violations+Thereof">Aalto University Code of Academic Integrity and Handling Violations Thereof</a></li>
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<p>All the course material is available in a <a href="https://github.com/avehtari/BDA_course_Aalto">git repo</a> (and 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>
<p>The material will be updated during the course. Exercise instructions and slides will be updated at latest on Monday of the corresponding week. The updated material will appear on the web pages, or you can clone the repo and pull before checking new material. If you don’t want to learn git, you can download the latest <a href="https://github.com/avehtari/BDA_course_Aalto/archive/master.zip">zip file</a>.</p>
<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 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. Aalto library has also copies. 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_all.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 Finnish, see e.g. <a href="http://math.aalto.fi/~lleskela/LectureNotes003.html">Stokastiikka ja tilastollinen ajattelu</a></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><a href="https://xcelab.net/rm/statistical-rethinking/">Richard McElreath: 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><a href="https://www.bayesrulesbook.com/">Johnson, Ott, and Dogucu: Bayes Rules! An Introduction to Applied Bayesian Modeling</a></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><a href="https://mycourses.aalto.fi/course/view.php?id=37048">MyCourses</a> is used for some intial announcements, linking to Zulip and Peergrade, and some questionnaires.</li>
<li>The primary communication channel is the Zulip course chat (link in MyCourses)
<ul>
<li>Don’t ask via email or direct messages. By asking via common streams 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>Login with Aalto account to the Zulip course chat</li>
<li>If you have any questions, please ask in the public streams and get answers from course staff or other students (active students helping others will get bonus points)</li>
<li>In the chat system, we will have separate streams for each assignment and the project.</li>
<li>Stream <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 stream).</li>
<li>Any kind of feedback is welcome on stream <strong>#feedback</strong>.</li>
<li>We have also streams <strong>#r</strong>, <strong>#python</strong>, and <strong>#stan</strong> for questions that are not specific to assignments or the project.</li>
<li>Stream <strong>#queue</strong> is used as a queue for getting help during <a href="assignments.html#TA_sessions">TA sessions</a>.</li>
<li>The lecturer and teaching assistants have names with “(staff)” or “(TA)” in the end of their names.</li>
</ul></li>
<li>A weekly lecture time on campus includes times for questions and answers</li>
<li>If you need one-to-one help, please take part in the <a href="assignments.html#TA_sessions">TA sessions</a> and ask there.</li>
<li>If you find errors in material, post in <strong>#feedback</strong> stream 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.html">Assignments</a> (67%) and a <a href="project.html">project work with presentation</a> (33%). Minimum of 50% of points must be obtained from both the assignments and project work.</p>
</div>
<div id="schedule-2022" class="section level2">
<h2>Schedule 2022</h2>
<p>The course consists of 12 blocks in periods I and II. 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 style="width:100%;">
<colgroup>
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th align="left">Block</th>
<th>Readings</th>
<th>Lectures</th>
<th>Assignment</th>
<th>Lecture Date</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><br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=a441a604-ba91-4f51-b88c-af0700b9f2b1">2022 Lecture 1.1 Introduction</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=a04b493c-1b94-4fe4-b9ef-af0700ca7b48">2022 Lecture 1.2 Course practicalities</a></td>
<td><a href="assignments/assignment1.pdf">Assignment 1</a>, <br> <a href="assignments/assignment1_rubric.html">Rubric questions</a></td>
<td>5.9.</td>
<td>11.9.</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=66aa86ca-9346-4f7f-af76-af0e00b9cb83">2022 Lecture 2.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=0b59d2d7-8e48-406c-a51e-af0e00ca3035">2022 Lecture 2.2</a></td>
<td><a href="assignments/assignment2.pdf">Assignment 2</a>, <br> <a href="assignments/assignment2_rubric.html">Rubric questions</a></td>
<td>12.9.</td>
<td>18.9.</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=ca0ea8b9-31f0-406d-9d2f-af1600f9826d">2022 Lecture 3.1</a> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=619d3f16-6af6-4e1b-af70-af1601162a13">2022 Lecture 3.2</a></td>
<td><a href="assignments/assignment3.pdf">Assignment 3</a>, <br> <a href="assignments/assignment3_rubric.html">Rubric questions</a></td>
<td>19.9.</td>
<td>25.9.</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=ab7b792b-50ef-42f4-90f5-af1c00b98b48">2022 Lecture 4.1</a>, <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=fe23cc92-4d90-4f68-9148-af1c00e29790">2022 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>26.9.</td>
<td>2.10.</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=96b8a8a9-60b6-4a7b-bca9-af2300b98547">2022 Lecture 5.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=fdd0c498-4e4a-4586-8ba9-af2300ca712d">2022 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>3.10.</td>
<td>9.10.</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">Old Lecture 6.1</a>, <br><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e60ba1a9-f752-4b0a-88c6-aae600caa61a">Old 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>10.10.</td>
<td>23.10.</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">Old Lecture 7.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c822561c-f95d-44fc-a1d0-aaf400d9fae3">Old 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>24.10.</td>
<td>6.11.</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">Old Lecture 8.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d7849131-0afd-4ae6-ad64-aafb00da36f4">Old Lecture 8.2</a></td>
<td>Start project work</td>
<td>31.10.</td>
<td>N/A</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">Old Lecture 9.1</a>, <br> Optional: <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=b0299d53-9454-4e33-9086-ab0200db14ee">Old Lecture 9.2</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=4b6eeb48-ae64-4860-a8c3-ab0200e40ad8">Old 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>7.11.</td>
<td>13.11.</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">Old 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>14.11.</td>
<td>20.11.</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">Old Lecture 11.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=a8e38a95-a944-4f3d-bf95-ab1000dbdf73">Old Lecture 11.2</a></td>
<td>Project work</td>
<td>21.11.</td>
<td>N/A</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">Old Lecture 12.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c43c862a-a5a4-45da-9b27-ab1700e12012">Old Lecture 12.2</a></td>
<td>Project work</td>
<td>28.11.</td>
<td>N/A</td>
</tr>
<tr class="odd">
<td></td>
<td align="left">13. Project evaluation</td>
<td></td>
<td></td>
<td></td>
<td>Project presentations: 12.-16.12.</td>
<td>Evaluation week</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, QA sessions, TA sessions, prerequisites, chat, etc.</p>
<ul>
<li>Login with Aalto account to the Zulip course chat with link in MyCourses</li>
<li>Signin to <a href="https://www.peergrade.io">Peergrade</a> with link in MyCourses. <strong>The link is currently broken. We have contacted MyCourses and Peergrade supports</strong></li>
<li>Introduction/practicalities <strong>lecture Monday 5.9. 14:15-16</strong>, hall A, Otakaari 1**<br>
<ul>
<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%224a7f385e-fdb1-4382-bfd0-af0700b7fc46%22">2022 Lecture videos 1.1 and 1.2 in Panopto</a></li>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Read BDA3 Chapter 1
<ul>
<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 Sunday 11.9. 23:59</strong>
<ul>
<li>We highly recommend to submit all assignments Friday before 3pm so that you can get TA help before submission. As the course has students who work weekdays (e.g. FiTech students), the late submission until Sunday night is allowed, but we can’t provide support during the weekends.</li>
<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.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>Get help in TA sessions 7.9. 12-16, 8.9. 12-14, 9.9. 12-14
<ul>
<li>in Sisu these are marked as exercise sessions, but we call them TA sessions</li>
<li>these are optional and you can choose which one to join</li>
<li>see more info about <a href="assignments.html#TA_sessions">TA sessions</a></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><strong>Lecture Monday 12.9. 14:15-16, hall T1, CS building</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>2022 <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=66aa86ca-9346-4f7f-af76-af0e00b9cb83">Lecture 2.1</a>, <br> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=0b59d2d7-8e48-406c-a51e-af0e00ca3035">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.</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 Sunday 18.9. 23:59</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 14.9.</li>
<li>Reflect on your feedback</li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 14.9. 12-16, 15.9. 12-14, 16.9. 12-14.</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><strong>Lecture Monday 19.9.. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>2022 <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ca0ea8b9-31f0-406d-9d2f-af1600f9826d">Lecture 3.1</a> <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=619d3f16-6af6-4e1b-af70-af1601162a13">Lecture 3.2</a> on multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation. BDA3 Ch 3.</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 Sunday 25.9. 23:59</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 21.9., and reflect on your feedback.</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 21.9. 12-16, 22.9. 12-14, 23.9. 12-14.</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><strong>Lecture Monday 26.9. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>2022 <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ab7b792b-50ef-42f4-90f5-af1c00b98b48">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=fe23cc92-4d90-4f68-9148-af1c00e29790">Lecture 4.2</a> on direct simulation, curse of dimensionality, rejection sampling, and importance sampling. BDA3 Ch 10.</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 Sunday 2.10. 23:59</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 28.9., and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 28.9. 12-16, 29.9. 12-14, 30.9. 12-14.</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><strong>Lecture Monday 3.10. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>2022 <a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=96b8a8a9-60b6-4a7b-bca9-af2300b98547">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=fdd0c498-4e4a-4586-8ba9-af2300ca712d">Lecture 5.2</a> on warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3 Ch 11.</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 Sunday 9.10. 23:59</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 5.10., and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 5.10. 12-16, 6.10. 12-14, 7.10. 12-14.</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><strong>Lecture Monday 10.10. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Previous year videos <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>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>DeadlineSunday 23.10. 23:59</strong> (two weeks for this assignment)
<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 12.10., and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 12.10. 12-16, 13.10. 12-14, 14.10. 12-14.</li>
<li>Start reading Chapter 5 + Stan material, see instructions below</li>
<li>No Lecture on evaluation week 25.10.</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><strong>Lecture Monday 24.10. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Previous year videos <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.</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 Sunday 6.11. 23:59</strong> (two weeks for this assignment)
<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 26.10., and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 26.10. 12-16, 27.10. 12-14, 28.10. 12-14.</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><strong>Lecture Monday 31.10. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Previous year videos <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.</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><a href="assignments.html#TA_sessions">TA sessions</a> 2.11. 12-16, 3.11. 12-14, 4.11. 12-14.</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><strong>Lecture Monday 7.11. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Previous year videos <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.</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>Sunday 13.11. 23:59</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 9.11., and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 9.11. 12-16, 10.11. 12-14, 11.11. 12-14.</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><strong>Lecture Monday 14.11. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Previous year videos <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.</li>
<li>Project presentation info will be updated soon.</li>
<li>Make and submit <a href="assignments/assignment9.pdf">Assignment 9</a>. <strong>Sunday 20.11. 23:59</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 N/A, and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 16.11. 12-16, 17.11. 12-14, 18.11. 12-14.</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><strong>Lecture Monday 21.11. 14:15-16</strong>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li>Previous year videos <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.</li>
<li>No new assignment. Work on project. TAs help with projects.
<ul>
<li>Review Assignment 9 done by your peers before 23:59 16.11., and reflect on your feedback</li>
</ul></li>
<li><a href="assignments.html#TA_sessions">TA sessions</a> 23.11. 12-16, 24.11. 12-14, 25.11. 12-14.</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><p><strong>Lecture Monday 28.11. 14:15-16</strong></p>
<ul>
<li><a href="https://github.com/avehtari/BDA_course_Aalto/tree/master/slides">Slides</a></li>
</ul></li>
<li><p>These lectures are optional, but especially the lecture on hypothesis testing and variable selection is useful for project work.</p></li>
<li><p>Previous year videos <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.</p></li>
<li><p>Work on project. TAs help with projects. <strong>Project deadline 6.12. 23:59</strong></p></li>
<li><p><a href="assignments.html#TA_sessions">TA sessions</a> 30.11. 12-16, 1.12. 12-14, 2.12. 12-14.</p></li>
</ul>
</div>
<div id="project-evaluation" class="section level3">
<h3>13) Project evaluation</h3>
<ul>
<li><p>Project report deadline 6.12. 23:59 (submit to peergrade).</p>
<ul>
<li>Review project reports done by your peers before9.12. 23:59, and reflect on your feedback.</li>
</ul></li>
<li><p>Project presentations 12.-16.12. (evaluation week)</p></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>
<li><a href="FAQ.html#How_to_use_R_and_RStudio_remotely">or use RStudio remotely</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 id="finnish-terms" class="section level2">
<h2>Finnish terms</h2>
<p>Sanasta “bayesilainen” esiintyy Suomessa muutamaa erilaista kirjoitustapaa. Muoto “bayesilainen” on muodostettu yleisen vieraskielisten nimien taivutussääntöjen mukaan: <em>“Jos nimi on kirjoitettuna takavokaalinen mutta äännettynä etuvokaalinen, kirjoitetaan päätteseen tavallisesti takavokaali etuvokaalin sijasta, esim. Birminghamissa, Thamesilla.” Terho Itkonen, Kieliopas, 6. painos, Kirjayhtymä, 1997.</em></p>
<ul>
<li><a href="extra_reading/sanasto.pdf">Lyhyt englanti-suomi sanasto kurssin termeistä</a></li>
</ul>
</div>
</div>
</div>
</div>
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