This repository contains my solutions to the assignments in the book: "A Student’s Guide to Bayesian Statistics" by Ben Lambert. I will update the repository with my solutions continuously.
Each chapter of the book has its corresponding folder in this repository. These solutions consist of Python code as well as pdfs.
Let me know (by posting an issue or via email: hotti@kth.se) if you have any questions or would like to discuss a certain solution or assignment!
The code for this section can be found: HERE The report can be found: HERE
The code for this section can be found: HERE The report can be found: HERE
The report can be found: HERE
The report can be found: HERE
The report can be found: HERE
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%20prior/prior_dir_10_10_10.png)
%20prior/posterior_dir_10_10_10.png)
The report can be found: HERE
Using a Binomial likelihood, a Beta prior and an symmetric Normal jumping kernel.
Using a Beta-Binomial likelihood, a Gamma prior and an assymmetric log-Normal jumping kernel.
Using a Poisson Likelihood, a Gamma prior, a Beta Prior, a log-Normal jumping kernel and a beta jumping kernel.
The report can be found: HERE
Using Gibbs sampling to estimate the point in time when legislative and societal changes caused a reduction in coal mining disasters in the UK. The number of disasters per year pre and post legislations were modeled using Poisson Likelihoods: Possion(lambda_1), Possion(lambda_2) with Gamma priors. The point in time when the new legislations were enacted is called n.
