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Probabilistic Graphical Models 1 - Representation

Last updated Sep 9, 2022

About the Course

This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

# Week 1

# Introduction and Overview

# Welcome

# Overview and Motivation

# Distributions

# Factors

# Bayesian Network Fundamentals

# Semantics & Factorizations

# Reasoning Patterns

# Flow of Probabilistic Influence

# Bayesian Networks: Independencies

# Conditional Independence

# Independencies in Bayesian Networks