If I should ever have a tattoo, that would be

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

Contents

# Prerequisite

- 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

- L1: Introduction
- L2: Basic Concepts and Models
- L3: Normal Approximation
- L4: Hierarchical Models
- L5: Bayesian Computations
- L6: Model Checking

# Part II: Bayesian Advanced Modeling

- A1: Bayesian Nonparametric Modeling | R Regression Spline Code | Related Paper
- A2: Bayesian Mixture Models | R Normal Mixture Code | Related Paper
- A3: 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.