دانلود کتاب Graphical models : foundations of neural computation

1 Probabilistic Independence Networks for Hidden Markov Probability Models / Padhraic Smyth, David Heckerman, Michael I. Jordan 1 --2 Learning and Relearning in Boltzmann Machines / G.E. Hinton, T.J. Sejnowski 45 --3 Learning in Boltzmann Trees / Lawrence Saul, Michael I. Jordan 77 --4 Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space / Geoffrey E. Hinton 89 --5 Attractor Dynamics in Feedforward Neural Networks / Lawrence K. Saul, Michael I. Jordan 97 --6 Efficient Learning in Boltzmann Machines Using Linear Response Theory / H.J. Kappen, F.B. Rodriguez 121 --7 Asymmetric Parallel Boltzmann Machines Are Belief Networks / Radford M. Neal 141 --8 Variational Learning in Nonlinear Gaussian Belief Networks / Brendan J. Frey, Geoffrey E. Hinton 145 --9 Mixtures of Probabilistic Principal Component Analyzers / Michael E. Tipping, Christopher M. Bishop 167 --10 Independent Factor Analysis / H. Attias 207 --11 Hierarchical Mixtures of Experts and the EM Algorithm / Michael I. Jordan, Robert A. Jacobs 257 --12 Hidden Neural Networks / Anders Krogh, Soren Kamaric Riis 291 --13 Variational Learning for Switching State-Space Models / Zoubin Ghahramani, Geoffrey E. Hinton 315 --14 Nonlinear Time-Series Prediction with Missing and Noisy Data / Volker Tresp, Reimar Hofmann 349 --15 Correctness of Local Probability Propagation in Graphical Models with Loops / Yair Weiss 367.