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A view of the EM algorithm that justifies incremental, sparse, and other variants

Authors:

Radford M. Neal,

Geoffrey E. HintonAuthors Info & Claims

Learning in graphical models

February 1999

Pages 355 - 368

Published: 01 February 1999 Publication History

Abstract

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Finesso LSpreij P(2023)The Inverse Problem of Positive AutoconvolutionIEEE Transactions on Information Theory10.1109/TIT.2023.324440769:6(4081-4092)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TIT.2023.3244407
Lin QLiu JXu FPan YZhu YZhang LZhao TAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Incorporating Context Graph with Logical Reasoning for Inductive Relation PredictionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531996(893-903)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531996
Lin PNeil MFenton NDementiev E(2022)Region‐based estimation of the partition functions for hybrid Bayesian network modelsInternational Journal of Intelligent Systems10.1002/int.2297337:11(8897-8927)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1002/int.22973
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A view of the EM algorithm that justifies incremental, sparse, and other variants
1. Computing methodologies
2. Mathematics of computing
  1. Information theory
  2. Probability and statistics

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Learning in graphical models

February 1999

634 pages

ISBN:0262600323

Editor:
Michael I. Jordan

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MIT Press

Cambridge, MA, United States

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Published: 01 February 1999

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Finesso LSpreij P(2023)The Inverse Problem of Positive AutoconvolutionIEEE Transactions on Information Theory10.1109/TIT.2023.324440769:6(4081-4092)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TIT.2023.3244407
Lin QLiu JXu FPan YZhu YZhang LZhao TAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Incorporating Context Graph with Logical Reasoning for Inductive Relation PredictionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531996(893-903)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531996
Lin PNeil MFenton NDementiev E(2022)Region‐based estimation of the partition functions for hybrid Bayesian network modelsInternational Journal of Intelligent Systems10.1002/int.2297337:11(8897-8927)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1002/int.22973
Shen WGuo YWang YZhao KWang BYuille A(2021)Deep Differentiable Random Forests for Age EstimationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.293729443:2(404-419)Online publication date: 1-Feb-2021
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Karimi BLi P(2021)Two-Timescale Stochastic EM Algorithms2021 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT45174.2021.9518283(890-895)Online publication date: 12-Jul-2021
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A view of the EM algorithm that justifies incremental, sparse, and other variants

On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures

A BYY Split-and-Merge EM Algorithm for Gaussian Mixture Learning

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