Lessons

Bayesian Statistics: Techniques and Models
1.01 (V) Course Introduction

Bayesian Statistics: Techniques and Models
1.02 (R) Module 1 Assignments and Materials

Bayesian Statistics: Techniques and Models
1.03 (V) Objectives

Bayesian Statistics: Techniques and Models
1.04 (V) Modeling Process

Bayesian Statistics: Techniques and Models
1.05 (Q) Lesson 1

Bayesian Statistics: Techniques and Models
1.06 (D) Statistical Modeling Process

Bayesian Statistics: Techniques and Models
1.07 (V) Components of Bayesian Models

Bayesian Statistics: Techniques and Models
1.08 (V) Model Specification

Bayesian Statistics: Techniques and Models
1.09 (V) Posterior Deviation

Bayesian Statistics: Techniques and Models
1.10 (V) Nonconjugate Models

Bayesian Statistics: Techniques and Models
1.11 (Q) Lesson 2

Bayesian Statistics: Techniques and Models
1.12 (R) Reference

Bayesian Statistics: Techniques and Models
1.13 (V) Monte Carlo Integration

Bayesian Statistics: Techniques and Models
1.14 (V) Monte Carlo Error and Marginalization

Bayesian Statistics: Techniques and Models
1.15 (V) Computing Examples

Bayesian Statistics: Techniques and Models
1.16 (V) Computing Monte Carlo Error

Bayesian Statistics: Techniques and Models
1.17 (R) Markoc Chains