Topics in Bayesian Computing

Class of STAT 946, Spring 2020
University of Waterloo

2022-11-29

Foreword

This e-book is a collection of modules on contemporary topics in Bayesian computing. It was written as a homework assignment by the students of STAT 946 taught in Spring 2020 at the University of Waterloo.

Table of Contents

• Basics of Bayesian Inference: Martin Lysy.

• Basics of Markov Chain Monte Carlo: Martin Lysy.

• Hamiltonian Monte Carlo: Yunfeng Yang and Ye Meng.

• Stochastic Differential Equationss: Hyunjin-Dominique Cho, Hytham Farah, and Francis Han.

• Particle Filters: Jason Hou-Liu and Michelle Ko.

• Gaussian Process Regression: Meixi Chen and Yuan Tian.

• Bayesian Nonparametrics: JunYong Tong and Nicholas Torenvliet.

• Laplace Approximation: Augustine Wigle.

• Approximate Bayesian Methods: Bo Yuan Chang.

• Variational Inference: Tim Dockhorn.

Contributing

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