Description
Variational Bayesian Learning Theory provides a rigorous treatment of variational inference, one of the most important approximate inference techniques in probabilistic machine learning. The book systematically develops the theoretical foundations while maintaining focus on practical applications.
Written by leading experts in the field, this Cambridge University Press publication covers key topics including the variational Bayes framework, approximate posterior inference, and the connections between information geometry and variational methods. The authors present both classical results and recent advances, making the material accessible to graduate students and researchers.
The text emphasizes the interplay between theory and practice, with detailed explanations of how variational Bayesian methods are implemented in real-world machine learning problems. Readers will gain deep insights into convergence properties, approximation quality, and the mathematical tools necessary for developing and analyzing new variational algorithms.
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