Statistical inference with probabilistic graphical models

DOI10.1093/ACPROF:OSO/9780198743736.003.0001zbMATH Open1397.62197arXiv1409.4928OpenAlexW27988576MaRDI QIDQ2990196

Publication date: 29 July 2016

Published in: Statistical Physics, Optimization, Inference, and Message-Passing Algorithms (Search for Journal in Brave)

Abstract: These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka Zdeborova from CEA Saclay & CNRS, and Riccardo Zecchina from Politecnico Torino. This lecture of Devavrat Shah (MIT) covers the basics of inference and learning. It explains how inference problems are represented within structures known as graphical models. The theoretical basis of the belief propagation algorithm is then explained and derived. This lecture sets the stage for generalizations and applications of message passing algorithms.

Full work available at URL: https://arxiv.org/abs/1409.4928

zbMATH Keywords

statistical inference inference Markov random fields probabilistic graphical models junction tree parameter learning belief propagation

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Applications of graph theory (05C90) Learning and adaptive systems in artificial intelligence (68T05) Information theory (general) (94A15) Network protocols (68M12)

Related Items (7)

Graphical models for statistical inference and data assimilation ⋮ Inference of structures of models of probabilistic dependences from statistical data ⋮ A probabilistic graphical model based stochastic input model construction ⋮ Inference With Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach ⋮ Graphical models for inference under outcome-dependent sampling ⋮ Probabilistic graphical models. Principles and applications ⋮ Title not available (Why is that?)

This page was built for publication: Statistical inference with probabilistic graphical models

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q2990196)