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I need a reference request for bayesian inference for self-study, I'm look for something containing:

1-Fundamentals about prior and posterior distribution, conflict between prior and posterior

2- General principles about Bayesian inference: likelihood, sufficiency, alacrity, not identifiability.

3- Estimation: utility and loss. Interchangeability the Finetti theorem

4- Priors: own, improper, combined, informative and uninformative.

5- Model Comparison: Bayes factor, sensitivity.

6- Bayesian hypothesis testing and credibility regions.

7- . Numerical Methods: Classical methods approach: numerical integration, integration by Monte Carlo and approach analytical Laplace. Bayesian sampling and MCMC (MCMC Methods).

Actually I'm using "Bayesian Choice, Robert C", but this has no solution to guide. Someone would have a book to show me or even some open courses in any university.

Thank you

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Gelman's Bayesian Data Analysis satisfies this list. There's a solution guide for some problems online.

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I'll second the recommendation of Bayesian Data Analysis. It's thorough and written by some of the top people in the field.

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