Bayes

TL;DR (Summary) This is a solutions site for the end-of-chapter exercises in A First Course in Bayesian Statistical Methods / 標準 ベイズ統計学 (Japanese edition). I started it during my Master’s degree and still update it, albeit at a very slow pace. Recently, after just a few updates, the site went from not appearing in Google search results at all to ranking highly. Here’s a memo on what I did 📝. Motivation for Improvement: “Doesn’t Show Up in Search Results at All” I put a lot of effort into this site during my Master’s, second only to my thesis, and I thought there would be decent demand from readers of Hoff, “A First Course in Bayesian Statistical Methods”. ...

Published the source code for: Example Solutions for Hoff’s Exercises GitHub This site was published exactly two years ago and has been updated gradually since then. Previously, I had written on the site asking people to contact me via email if they found any mistakes or typos, but ultimately, I never received any such contact in two years 😭 (I did receive thank-you emails from researchers overseas, which made me very happy.) ...

Introduction I have updated Answers of exercises on Hoff, A first course in Bayesian statistical methods (added 10-4), so I’d like to briefly touch upon its content in this blog post as well. First, let me briefly explain the relationship between this problem and the textbook content. I previously wrote an article titled Hoff/標準ベイズのM-Hアルゴリズムがworkすることの証明でつまずいた話 (A story about stumbling on the proof that the M-H algorithm in Hoff/Standard Bayes works), and in Hoff (2009), the proof that the M-H algorithm works in Chapter 10 is done in the following steps: ...

Introduction Regarding the proof presented in Hoff (2009) and Hoff et al. (2022), Section 10.4.2 “Why does the Metropolis-Hastings algorithm work?”, I encountered a point of confusion, so I decided to write about it to organize my thoughts. Hoff’s Proof Flow In Hoff (2009) and Hoff et al. (2022), the proof explaining why the Markov chain generated by the M-H algorithm can approximate the target distribution \(p_0\) proceeds as follows: ...