Probabilistic Graphical Models Principles and Techniques

Probabilistic Graphical Models: Principles and Techniques / Daphne Koller and Nir Friedman. p. cm. – (Adaptive computation and machine learning) Includes bibliographical references and index. ISBN 978-0-262-01319-2 (hardcover : alk. paper) 1. Graphical modeling (Statistics) 2. Bayesian statistical decision theory—Graphic methods. I. Koller ...
Inferring cellular networks using probabilistic graphical models Science, Nir Friedman, v303 p799, 6 Feb 2004. Recommended introductory reading Books In reverse chronological order. Daphne Koller and Nir Friedman, "Probabilistic graphical models: principles and techniques", MIT Press 2009
COL776 Learning Probabilistic Graphical Models. 4 credits (3-0-2) Pre-requisites: MTL106 OR Equivalent. Basics: Introduction. Undirected and Directed Graphical Models. Bayesian Networks. Markov Networks. Exponential Family Models. Factor Graph Representation. Hidden Markov Models. Conditional Random Fields. Triangulation and Chordal Graphs.
04/06/2019 · Keywords: directed graphical causal models, causal discovery, conditional independence, statistical independence, structural equation models, non-Gaussian distribution, non-linear models. Citation: Glymour C, Zhang K and Spirtes P (2019) Review of Causal Discovery Methods Based on Graphical Models. Front. Genet. 10:524. doi: …
Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228). Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning. Available free online. Hastie, Tibshirani, and Friedman. The elements of statistical learning.

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