Presents an exploration of issues related to learning within the graphical model formalism. This text covers topics such as: inference for Bayesian networks; Mo

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making deci

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book Gain in-depth knowledge o

A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computati

The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate sta