Variational Bayesian Learning Theory

Β· Β·
Β· Cambridge University Press
Π•Π»Π΅ΠΊΡ‚Ρ€ΠΎΠ½Π½Π° ΠΊΠ½ΠΈΠ³Π°
561
Π‘Ρ‚Ρ€Π°Π½ΠΈΡ†ΠΈ

Всичко Π·Π° Ρ‚Π°Π·ΠΈ Π΅Π»Π΅ΠΊΡ‚Ρ€ΠΎΠ½Π½Π° ΠΊΠ½ΠΈΠ³Π°

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this the...

ΠžΡ‚ΠΊΡ€ΠΈΠΉΡ‚Π΅ ΠΎΡ‰Π΅

Π—Π° Π°Π²Ρ‚ΠΎΡ€Π°

Shinichi Nakajima is a senior researcher at Technische UniversitΓ€t Berlin. His research interests include the theory and applications of machine learning, and he has published papers at numerous conferences and in journals such as the Journal of Machine Learning Research, the Machine Learning Journal, Neural Computation, and IEEE Transactions on Signal Processing. He currently serves as an area chair for NIPS and an action Editor for Digital Signal Processing.

Kazuho Watanabe is a lecturer at Toyohashi University of Technology. His research interests include statistical machine learning and information theory, and he has published papers at numerous conferences and in journals such as the Journal of Machine Learning Research, the Machine Learning Journal, IEEE Transactions on Information Theory, and IEEE Transactions on Neural Networks and Learning Systems.

Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Complexity Science and Engineering at the University of Tokyo. His research interests include the theory, algorithms, and applications of machine learning. He has written several books on machine learning, including Density Ratio Estimation in Machine Learning (Cambridge, 2012). He served as program co-chair and general co-chair of the NIPS conference in 2015 and 2016, respectively, and received the Japan Academy Medal in 2017.

Shinichi Nakajima is a senior researcher at Technische UniversitΓ€t Berlin. His research interests include the theory and applications of machine learning, and he has published papers at numerous conferences and in journals such as the Journal of Machine Learning Research, the Machine Learning Jou...

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Π—Π° Π΄Π° Ρ‡Π΅Ρ‚Π΅Ρ‚Π΅ Π½Π° устройства с Π΅Π»Π΅ΠΊΡ‚Ρ€ΠΎΠ½Π½ΠΎ мастило, ΠΊΠ°Ρ‚ΠΎ Π½Π°ΠΏΡ€ΠΈΠΌΠ΅Ρ€ Π΅Π»Π΅ΠΊΡ‚Ρ€ΠΎΠ½Π½ΠΈΡ‚Π΅ Ρ‡Π΅Ρ‚Ρ†ΠΈ ΠΎΡ‚ Kobo, трябва Π΄Π° ΠΈΠ·Ρ‚Π΅Π³Π»ΠΈΡ‚Π΅ Ρ„Π°ΠΉΠ» ΠΈ Π΄Π° Π³ΠΎ ΠΏΡ€Π΅Ρ…Π²ΡŠΡ€Π»ΠΈΡ‚Π΅ Π½Π° устройството си. Π˜Π·ΠΏΡŠΠ»Π½Π΅Ρ‚Π΅ ΠΏΠΎΠ΄Ρ€ΠΎΠ±Π½ΠΈΡ‚Π΅ инструкции Π² ΠŸΠΎΠΌΠΎΡ‰Π½ΠΈΡ Ρ†Π΅Π½Ρ‚ΡŠΡ€, Π·Π° Π΄Π° ΠΏΡ€Π΅Ρ…Π²ΡŠΡ€Π»ΠΈΡ‚Π΅ Ρ„Π°ΠΉΠ»ΠΎΠ²Π΅Ρ‚Π΅ Π² ΠΏΠΎΠ΄Π΄ΡŠΡ€ΠΆΠ°Π½ΠΈΡ‚Π΅ Π΅Π»Π΅ΠΊΡ‚Ρ€ΠΎΠ½Π½ΠΈ Ρ‡Π΅Ρ‚Ρ†ΠΈ.

ΠžΡ‰Π΅ ΠΎΡ‚ Shinichi Nakajima

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