## Stat 375 : ReferencesThere does not exist a textbook that matches exactly the topics treated in this course. These lecture notes
follow the topics treated in class, and wiil be updated and polished
as we proceed. Further useful material can be found in: M. Jordan (ed.), Learning in Graphical Models, MIT Press, 1998 M. Jordan and T. J. Sejnowski (eds.), Graphical Models, MIT Press, 2001 M. Wainwright and M. Jordan, Graphical models, exponential families, and variational inference, Foudations and Trends in Machine Learning, 2008 M. Mézard and A. Montanari, Information, Physics and Computation, Oxford University Press, 2009 D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, The MIT Press, 2009
## Additional ReadingA lot of useful material can be found in recent research papers. We will propose a selection of such papers throughout the term. Reading the papers is not required but being curious and browsing through them is strongly recommended. More papers will be added Access to these papers is restricted to the Stanford community, and requires authentication from outside the stanford.edu domain. F.R. Kschischang, B.J. Frey, and H.-A. Loeliger, Factor Graphs and the Sum-Product Algorithm J.S. Yedidia, W.T. Freeman, and Y. Weiss, Constructing free energy approximations and generalized belief propagation algorithms J.S. Yedidia, An Idiosyncratic Journey Beyond Mean Field Theory M.J. Wainwright, T.S. Jaakkola, and A.S. Willsky, MAP Estimation Via Agreement on Trees: Message-Passing and Linear Programming M.J. Wainwright, T.S. Jaakkola, and A.S. Willsky, A New Class of Upper Bounds on the Log Partition Function A. Sinclair, Convergence Rates for Monte Carlo Experiments M. Jerrum and A. Sinclair, The Markov Chain Monte Carlo Method: An Approach to Approximate Counting and Integration R. Bubley and M. Dyer, Path Coupling: A Technique for Proving Rapid Mixing in Markov Chains S. Tatikonda and M. Jordan, Loopy Belief Propagation and Gibbs Measures D. Weitz, Combinatorial Criteria for Uniqueness of Gibbs Measures D. Weitz, Counting independent sets up to the tree threshold M.G. Luby, M. Mitzenmacher, M.A. Shokrollahi, D.A. Spielman, and V. Stenmann, Practical Loss-Resilient Codes D. Mitchell, B. Selman and H. Levesque, Hard and easy distributions of SAT problems T.J. Richardson and R. Urbanke, The Capacity of Low Density Parity Check Codes Under Message-Passing Decoding |