Machine Learning & Artificial Intelligence · Artificial Intelligence & Deep Learning

Probabilistic Graphical Models · Advanced Artificial Intelligence

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Rigorous Mathematical Treatment

Overview

Topics covered

  • Introduction
  • Probabilistic Graphical Models: The Conjunction of Graphs and Probability
  • Conceptual introduction and historical perspective
  • Fundamentals of high-dimensional probability theory
  • 2.1 The chain rule
  • 2.2 Absolute independence and conditional independence

Related concepts

Main connections

  • Conditional probability & independence
  • Bayes' theorem & priors
  • Markov chain Monte Carlo (MCMC)
  • Directed acyclic graphs (DAGs)
  • Maximum likelihood estimation
  • Confounding & selection bias

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

P(X1,X2,…,XN)=∏i=1NP(Xi∣X1,X2,…,Xi−1)
View LaTeX sourceP(X_1, X_2, \dots, X_N) = \prod_{i=1}^{N} P(X_i \mid X_1, X_2, \dots, X_{i-1})