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Variational Bayesian Learning Theory de Shinichi Nakajima

Variational Bayesian Learning Theory

Autor Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama
en Limba Engleză Paperback – 6 feb 2025
Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggests how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques.
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  Cambridge University Press – 6 feb 2025 27066 lei  3-5 săpt. +3378 lei  6-10 zile
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  Cambridge University Press – 10 iul 2019 85209 lei  6-8 săpt.

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Specificații

ISBN-13: 9781107430761
ISBN-10: 1107430763
Pagini: 560
Dimensiuni: 152 x 229 x 33 mm
Greutate: 0.9 kg
Editura: Cambridge University Press

Cuprins

1. Bayesian learning; 2. Variational Bayesian learning; 3. VB algorithm for multi-linear models; 4. VB Algorithm for latent variable models; 5. VB algorithm under No Conjugacy; 6. Global VB solution of fully observed matrix factorization; 7. Model-induced regularization and sparsity inducing mechanism; 8. Performance analysis of VB matrix factorization; 9. Global solver for matrix factorization; 10. Global solver for low-rank subspace clustering; 11. Efficient solver for sparse additive matrix factorization; 12. MAP and partially Bayesian learning; 13. Asymptotic Bayesian learning theory; 14. Asymptotic VB theory of reduced rank regression; 15. Asymptotic VB theory of mixture models; 16. Asymptotic VB theory of other latent variable models; 17. Unified theory.

Recenzii

'This book presents a very thorough and useful explanation of classical (pre deep learning) mean field variational Bayes. It covers basic algorithms, detailed derivations for various models (eg matrix factorization, GLMs, GMMs, HMMs), and advanced theory, including results on sparsity of the VB estimator, and asymptotic  properties (generalization bounds).' Kevin Murphy, Research scientist, Google Brain
'This book is an excellent and comprehensive reference on the topic of Variational Bayes (VB) inference, which is heavily used in probabilistic machine learning. It covers VB theory and algorithms, and gives a detailed exploration of these methods for matrix factorization and extensions. It will be an essential guide for those using and developing VB methods.' Chris Williams, University of Edinburgh

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