Apply suggestions from code review

Co-authored-by: Osvaldo Martin <[email protected]>

Author: michaelosthege

Diff for: README.md

@@ -1,12 +1,12 @@
 # Welcome to `pymc-experimental`
 
-As PyMC continues to mature and expand its functionality to accomodate more domains of application, we increasingly see cutting-edge methodologies, highly specialized statistical distributions, and complex models appear.
-While this adds to the functinoality and relevance of the project, it can also introduce instability and impose a burden on testing and quality control.
-To reduce the burden on the main `pymc` repository, this `pymc-experimental` respository can become the aggregator & testing ground for new additions to PyMC.
-This may include unusual probability distribitions, advanced model fitting algorithms, or any code that may be inappropriate to include in the `pymc` repository, but may want to be made available to users.
+As PyMC continues to mature and expand its functionality to accommodate more domains of application, we increasingly see cutting-edge methodologies, highly specialized statistical distributions, and complex models appear.
+While this adds to the functionality and relevance of the project, it can also introduce instability and impose a burden on testing and quality control.
+To reduce the burden on the main `pymc` repository, this `pymc-experimental` repository can become the aggregator and testing ground for new additions to PyMC.
+This may include unusual probability distributions, advanced model fitting algorithms, innovative yet not fully tested methods or any code that may be inappropriate to include in the `pymc` repository, but may want to be made available to users.
 
 The `pymc-experimental` repository can be understood as the first step in the PyMC development pipeline, where all novel code is introduced until it is obvious that it belongs in the main repository.
-We hope that this improves the stability and streamlines the testing overhead of the `pymc` respository.
+We hope that this organization improves the stability and streamlines the testing overhead of the `pymc` repository, while allowing users and developers to test and evaluate cutting-edge methods and not yet fully mature features.
 
 `pymc-experimental` would be designed to mirror the namespaces in `pymc` to make usage and migration as easy as possible.
 For example, a `ParabolicFractal` distribution could be used analogously to those in `pymc`:
@@ -27,21 +27,21 @@ with pm.Model():
 
 ### What belongs in `pymc-experimental`?
 
-- newly-implemented statistical methods, for example diagnostics or step methods
+- newly-implemented statistical methods, for example step methods or model construction helpers
 - distributions that are tricky to sample from or test
 - infrequently-used fitting methods or distributions
 - any code that requires additional optimization before it can be used in practice
 
 
 ### What does not belong in `pymc-experimental`?
 - Case studies
-- Implementations that cannot be applied generically
+- Implementations that cannot be applied generically, for example because they are tied to variables from a toy example
 
 
 ### Should there be more than one add-on repository?
 
 Since there is a lot of code that we may not want in the main repository, does it make sense to have more than one additional repository?
-For exmaple, `pymc-experimental` may just include methods that are not fully developed, tested and trusted, while code that is known to work well and has adequate test coverage could reside in a `pymc-extras` (or similar) repository.
+For example, `pymc-experimental` may just include methods that are not fully developed, tested and trusted, while code that is known to work well and has adequate test coverage, but is still too specialized to become part of `pymc` could reside in a `pymc-extras` (or similar) repository.
 
 
 ### Unanswered questions & ToDos