EE378A: Course Outline

Stanford University, Spring Quarter 2012-13
  • Introduction

  • A framework for general Bayesian Inference

  • Markov triplets, Conditional independence, and undirected graphical models

  • Inference and state estimation for Hidden Markov Processes (HMPs)

  • Inference on Markov Random Fields

  • Markov Chain Monte Carlo and Importance sampling

  • Particle and approximate filtering

  • Universal denoising

Optional topics according to remaining time and interest:

  • Connections between Information and Estimation

  • Inference under logarithmic loss

  • Causality inference