Merge branch 'main' of github.com:maxbiostat/BayesianStatisticsCourse into main

Author: maxbiostat

README.md

@@ -80,7 +80,7 @@ Syllabus [here](https://emap.fgv.br/disciplina/doutorado/estatistica-bayesiana)
 
 ## Lecture 9: Applications I
 
-- Ever wondered what to do when both the number of trials and success probability are unknown in a binomial model? Well, [this](https://pluto.mscc.huji.ac.il/~galelidan/52558/Material/Raftery.pdf) paper by Adrian Raftery has _an_ answer.
+- Ever wondered what to do when both the number of trials and success probability are unknown in a binomial model? Well, [this](https://pluto.mscc.huji.ac.il/~galelidan/52558/Material/Raftery.pdf) paper by Adrian Raftery has _an_ answer. See also [this](https://stats.stackexchange.com/questions/113851/bayesian-estimation-of-n-of-a-binomial-distribution) discussion with JAGS and Stan implementations.
 - [This](https://mc-stan.org/users/documentation/case-studies/golf.html) case study shows how to create a model from first (physical) principles.
 
 ## Lecture 10: Applications II
@@ -93,3 +93,5 @@ Syllabus [here](https://emap.fgv.br/disciplina/doutorado/estatistica-bayesiana)
 - See Jayne's monograph above.
 - See [Frequentism and Bayesianism: A Practical Introduction](https://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/) for a five-part discussion of the Bayesian vs Orthodox statistics.
 - [Why isn't everyone a Bayesian?](https://www2.stat.duke.edu/courses/Spring10/sta122/Handouts/EfronWhyEveryone.pdf) is a nice discussion of the trade-offs between paradigms by [Bradley Efron](https://statweb.stanford.edu/~ckirby/brad/).
+- [Holes in Bayesian statistics](https://iopscience.iop.org/article/10.1088/1361-6471/abc3a5#:~:text=Here%20are%20a%20few%20holes,Bayes%20factors%20fail%20in%20the) is a collection of holes in Bayesian data analysis, such as conditional probability in the quantum real, flat and weak priors, and model checking, written by Andrew Gelman and Yuling Yao.
+- [Bayesian Estimation with Informative Priors is Indistinguishable from Data Falsification](https://www.cambridge.org/core/journals/spanish-journal-of-psychology/article/bayesian-estimation-with-informative-priors-is-indistinguishable-from-data-falsification/FFAB96BDC5EE3C64B144ECF8F90F31E9) is paper that attempts to draw a connection between strong priors and data falsification. Not for the faint of heart.