Jordan M.I., Sejnowski. T.J. Graphical models: foundations of neural computation

MIT Press, 2001. — 433 p.Probabilistic Independence Networks for Hidden Markov Probability Models
Learning and Relearning in Boltzmann Machines
Learning in Boltzmann Trees
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space
Attractor Dynamics in Feedforward Neural Networks
Efficient Learning in Boltzmann Machines Using Linear Response Theory
Asymmetric Parallel Boltzmann Machines Are Belief Networks
Variational Learning in Nonlinear Gaussian Belief Networks
Mixtures of Probabilistic Principal Component Analyzers
Independent Factor Analysis
Hierarchical Mixtures of Experts and the EM Algorithm
Hidden Neural Networks
Variational Learning for Switching State-Space Models
Nonlinear Time-Series Prediction with Missing and Noisy Data
Correctness of Local Probability Propagation in Graphical Models with Loops.