I just made the same recommendation on a different post. My reasons for recommending Gelman over Jaynes here is the practical value of working through the problems in Gelman’s book. The problems Jaynes gives are focused on the theoretical, but the problems in BDA are applied, computational, and this is true from the beginning of the book: I used R for many of the problems at the end of chapter 2. By the end of chapter 3 I could already see ways I could apply the things I learned from BDA to my work as a Data Scientist. Jaynes also gives far fewer exercises—there are maybe 20-30 in the whole book, but in BDA there are 15 or so per chapter so far.
I read Jaynes’s book cover-to-cover, but should confess I’m only through chapter 3 of BDA. So maybe it goes off the deep end and I come back here in 6 months and withdraw my recommendation. But right now I’m recommending Bayesian Data Analysis.
I just made the same recommendation on a different post. My reasons for recommending Gelman over Jaynes here is the practical value of working through the problems in Gelman’s book. The problems Jaynes gives are focused on the theoretical, but the problems in BDA are applied, computational, and this is true from the beginning of the book: I used R for many of the problems at the end of chapter 2. By the end of chapter 3 I could already see ways I could apply the things I learned from BDA to my work as a Data Scientist. Jaynes also gives far fewer exercises—there are maybe 20-30 in the whole book, but in BDA there are 15 or so per chapter so far.
I read Jaynes’s book cover-to-cover, but should confess I’m only through chapter 3 of BDA. So maybe it goes off the deep end and I come back here in 6 months and withdraw my recommendation. But right now I’m recommending Bayesian Data Analysis.