Variational Bayesian Learning Theory
Autor Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyamaen Limba Engleză Hardback – 10 iul 2019
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Specificații
ISBN-13: 9781107076150
ISBN-10: 1107076153
Pagini: 558
Dimensiuni: 156 x 235 x 34 mm
Greutate: 0.91 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:New York, United States
ISBN-10: 1107076153
Pagini: 558
Dimensiuni: 156 x 235 x 34 mm
Greutate: 0.91 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:New York, United States
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
'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
Notă biografică
Descriere
This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.