If I should ever have a tattoo, that would be

$$p(\theta|y)= \frac{p(y|\theta)p(\theta)}{p(y)}$$

Table of Contents

# Prerequisites

- Basic knowledge of Statistics
- Basic knowledge of Statistical Computing

# Literature

- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014).
*Bayesian data analysis*(third edition), CRC press. - Robert, C., & Casella, G. (2010).
*Introducing Monte Carlo Methods with R*. Springer.

# Part I: Bayesian Theory and Models

- Introduction
- Basic Concepts and Models
- Normal Approximation
- Hierarchical Models
- Model Checking

# Part II: Bayesian Computations

- Introduction to Bayesian Simulation
- Sampling From Unknown Distribution
- Introduction to Markov chain Monte Carlo | R Code: Metropolis Metropolis-Hastings within Gibbs
- Monte Carlo Methods with Details
- Gibbs Sampler and Beyond

# Part III: Advanced Bayesian Modeling

- Bayesian Regression and Shrinkage
- Bayesian Variable Selection | Related Paper
- Bayesian Nonparametric Modeling | R Regression Spline Code | Related Paper
- Bayesian Mixture Models
- Bayesian Copula Modeling | R copula and VineCopula packages

# Software

# Computer code

# External Reading

- Howson, C. and Urbach, P., 2006.
*Scientific reasoning: the Bayesian approach*. third edition, Open Court Publishing.

If you have good command of elementary statistics, this is a good first book for someone who is interested in practical uncertainty quantification, that would like to learn about the Big Picture. It is a book about thinking and working like a Bayesian, rather than about techniques of Bayesian estimation.