An Introduction to Variational Methods for Graphical Models

[1]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[2]  Michael I. Jordan,et al.  Factorial Hidden Markov Models , 1995, Machine Learning.

[3]  Michael I. Jordan,et al.  Variational Probabilistic Inference and the QMR-DT Network , 2011, J. Artif. Intell. Res..

[4]  David J. C. MacKay,et al.  Comparison of Approximate Methods for Handling Hyperparameters , 1999, Neural Computation.

[5]  Michael I. Jordan,et al.  Improving the Mean Field Approximation Via the Use of Mixture Distributions , 1999, Learning in Graphical Models.

[6]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[7]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[8]  Brian Sallans,et al.  A Hierarchical Community of Experts , 1999, Learning in Graphical Models.

[9]  Michael I. Jordan,et al.  A Mean Field Learning Algorithm for Unsupervised Neural Networks , 1999, Learning in Graphical Models.

[10]  David J. C. Mackay,et al.  Introduction to Monte Carlo Methods , 1998, Learning in Graphical Models.

[11]  Robert Cowell,et al.  Introduction to Inference for Bayesian Networks , 1998, Learning in Graphical Models.

[12]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[13]  Jung-Fu Cheng,et al.  Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm , 1998, IEEE J. Sel. Areas Commun..

[14]  T. Jaakkola,et al.  Improving the Mean Field Approximation Via the Use of Mixture Distributions , 1999, Learning in Graphical Models.

[15]  Michael I. Jordan,et al.  Variational methods and the QMR-DT database , 1998 .

[16]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[17]  Neil D. Lawrence,et al.  Approximating Posterior Distributions in Belief Networks Using Mixtures , 1997, NIPS.

[18]  Michael I. Jordan,et al.  Probabilistic Independence Networks for Hidden Markov Probability Models , 1997, Neural Computation.

[19]  ModelsbyTommi S. Jaakkola.  Variational Methods for Inference and Estimation inGraphical , 1997 .

[20]  Michael I. Jordan,et al.  Variational methods for inference and estimation in graphical models , 1997 .

[21]  Michael I. Jordan,et al.  Recursive Algorithms for Approximating Probabilities in Graphical Models , 1996, NIPS.

[22]  Michael I. Jordan,et al.  Hidden Markov Decision Trees , 1996, NIPS.

[23]  Michael I. Jordan,et al.  Computing upper and lower bounds on likelihoods in intractable networks , 1996, UAI.

[24]  Rina Dechter,et al.  Bucket elimination: A unifying framework for probabilistic inference , 1996, UAI.

[25]  Michael I. Jordan,et al.  Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..

[26]  Geoffrey E. Hinton,et al.  Switching State-Space Models , 1996 .

[27]  Michael I. Jordan,et al.  Exploiting Tractable Substructures in Intractable Networks , 1995, NIPS.

[28]  Steve R. Waterhouse,et al.  Bayesian Methods for Mixtures of Experts , 1995, NIPS.

[29]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[30]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[31]  K. Bathe.  Finite Element Procedures , 1995 .

[32]  Uffe Kjærulff,et al.  Blocking Gibbs sampling in very large probabilistic expert systems , 1995, Int. J. Hum. Comput. Stud..

[33]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

[34]  Hill,et al.  Annealed Theories of Learning , 1995 .

[35]  Michael I. Jordan,et al.  Learning in Boltzmann Trees , 1994, Neural Computation.

[36]  Robert M. Fung,et al.  Backward Simulation in Bayesian Networks , 1994, UAI.

[37]  Uffe Kjærulff,et al.  Reduction of Computational Complexity in Bayesian Networks Through Removal of Weak Dependences , 1994, UAI.

[38]  Ross D. Shachter,et al.  Global Conditioning for Probabilistic Inference in Belief Networks , 1994, UAI.

[39]  Frank Jensen,et al.  Optimal junction Trees , 1994, UAI.

[40]  Denise Draper,et al.  Localized Partial Evaluation of Belief Networks , 1994, UAI.

[41]  Michael I. Jordan.  A statistical approach to decision tree modeling , 1994, COLT '94.

[42]  Walter R. Gilks,et al.  A Language and Program for Complex Bayesian Modelling , 1994 .

[43]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[44]  Michael Luby,et al.  Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard , 1993, Artif. Intell..

[45]  R. Martin Chavez,et al.  Approximating Probabilistic Inference in Bayesian Belief Networks , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  C. Galland.  The limitations of deterministic Boltzmann machine learning , 1993 .

[47]  Radford M. Neal.  Connectionist Learning of Belief Networks , 1992, Artif. Intell..

[48]  Prakash P. Shenoy,et al.  Valuation-Based Systems for Bayesian Decision Analysis , 1992, Oper. Res..

[49]  Gregory F. Cooper,et al.  An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network , 1991, Computers and biomedical research, an international journal.

[50]  Max Henrion,et al.  Search-Based Methods to Bound Diagnostic Probabilities in Very Large Belief Nets , 1991, UAI.

[51]  A. Hasman,et al.  Probabilistic reasoning in intelligent systems: Networks of plausible inference , 1991 .

[52]  Geoffrey E. Hinton,et al.  Mean field networks that learn to discriminate temporally distorted strings , 1991 .

[53]  D. Heckerman,et al.  ,81. Introduction , 2022 .

[54]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[55]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[56]  Uue Kjjrull.  Triangulation of Graphs { Algorithms Giving Small Total State Space Triangulation of Graphs { Algorithms Giving Small Total State Space , 1990 .

[57]  Eric Horvitz,et al.  Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources , 2013, UAI 1989.

[58]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[59]  Keiji Kanazawa,et al.  A model for reasoning about persistence and causation , 1989 .

[60]  Carsten Peterson,et al.  A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..

[61]  J. J. Sakurai,et al.  Modern Quantum Mechanics , 1986 .

[62]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[63]  H. Saunders.  Book Reviews : NUMERICAL METHODS IN FINITE ELEMENT ANALYSIS K.-J. Bathe and E.L. Wilson Prentice-Hall, Inc, Englewood Cliffs, NJ , 1978 .

[64]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[65]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .