Probabilistic Graphical Models

Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.

Type

Book

Intellectual base

Review or monograph

Year

2009

ISBN

9780262013192

Link

https://mitpress.mit.edu/9780262013192/probabilistic-graphical-models/

Cited from

cognitive_science, mathematics

Cited by

4 citations across 4 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Belief Formation
  • No social context, no motivated reasoning, no narrative — just the bare skeleton of belief formation, a treatment Koller and Friedman (2009) develop systematically in their reference on probabilistic graphical models.

    This sourceSystematic reference on Bayesian networks, Markov random fields, and the inference algorithms operationalizing belief-state representation and update in computational agents.

• Factorization
  • In probability and statistics it is factoring a joint distribution into conditionals and marginals along a graphical-model DAG — the explicit content of Bayesian networks — and latent-factor models that recover hidden generative factors.

    This sourceEstablishes the factorization of a joint distribution into local conditionals along a DAG (Bayesian networks) and the independence read off the product form.

Domain-specific

• Probabilistic Graphical Model
  • The graph is simultaneously a semantic map of dependence assumptions and a computational scaffold for inference and learning.
• Variable Elimination
  • An elimination ordering induces fill-in connections, and time/space are exponential in induced width/treewidth rather than simply the original number of variables.

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