Information Theory, Inference & Learning Algorithms8 reviews David J. C. MacKay Cambridge University Press, 2002 A must have... + pretty much indispensible + Outstanding book, especially for statisticians + Great wish it had more n option inverse problems + Great Book As Far As It Goes             Learning in Graphical Models (Adaptive Computation and Machine Learning)2 reviews The MIT Press, 1998 Simply Superb... + Recommended, but not the place to begin My area of research revolves around graphical models... Best Book... The book that introduced me as to how effective graphical models are... As stated in the editorial review, graphical model is the marriage between graph theory and probability and this book justifies the sacredness of this ...             Introduction to Graphical Modelling2 reviews David Edwards Springer, 2000 this is about directed graphs not graphics + directed graphs, path analysis and causality not the common statistical graphics Because graphic methods are very popular in statistics, when you read the title you might think this is a book on the use of graphics in statistics. That is not what the book is about. The directed graph on the cover might be a hint for some. The book deals with the theory of undirected and ...             Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and ...2 reviews Martin L. Puterman Wiley-Interscience, 2005 From the author of Approximate Dynamic Programming + Excellent and detailed, although focusing on exact algorithms only For anyone looking for an introduction to classic discrete state, discrete action Markov decision processes this is the last in a long line of books on this theory, and the only book you will need. The presentation covers this elegant theory very thoroughly, including all the major problem classes ...             Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference7 reviews Judea Pearl Morgan Kaufmann, 1988 Outstanding introduction to the field + a classic book + Elegant Discussion On Probabilistic Reasoning And Uncertainty Recently I needed to learn the principles of Bayesian networks quickly, so I bought three books: this one by Pearl, "Pattern Recognition and Machine Learning" by Bishop, and "Bayesian Artificial Intelligence" by Korb and Nicholson. Each has a very different audience and different set of strengths. ...             Bayesian Networks and Decision Graphs (Information Science and Statistics)8 reviews Finn V. Jensen, Thomas D. Nielsen Springer, 2007 Good Book + A very good introduction to Bayesian networks + Accessible introduction to Bayesian Networks For an introduction to the subject, this book is unequivocal in my experience with the literature. Great read that has propelled me forward into combining a bayesian network with a physical model to approach a very complex sediment transport problem.             Probability Theory: The Logic of Science17 reviews E. T. Jaynes Cambridge University Press, 2003 unbelievably charming and intelligent + Thought provoking + Flawed gems + On first reading + Great hard to find information             Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) The MIT Press, 2007 Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In Introduction to Statistical Relational Learning, ...             Learning Bayesian Networks (Artificial Intelligence)2 reviews Richard E. Neapolitan Prentice Hall, 2003 An excellent overview + Enjoying this book enormously In just a decade, Bayesian networks have went from being a mere academic curiosity to a highly useful field with myriads of applications. Indeed, the applications of Bayesian networks are wide-ranging and include disparate fields such as network engineering, bioinformatics, medical diagnostics, and ...             Pattern Recognition and Machine Learning (Information Science and Statistics)41 reviews Christopher M. Bishop Springer, 2007 Probably the best book for machine learning + Authorative text + Awesome I am a PhD student in machine learning. Bishop is really gifted and he explains very well basic and advanced concepts of machine learning. I would say that this book is much more comprehensive than Hastie's Statistical learning book The Elements of Statistical Learning. Very good illustrations and ...             Pattern Recognition and Machine Learning (Information Science and Statistics)41 reviews Christopher M. Bishop Springer, 2007 Probably the best book for machine learning + Authorative text + Awesome I am a PhD student in machine learning. Bishop is really gifted and he explains very well basic and advanced concepts of machine learning. I would say that this book is much more comprehensive than Hastie's Statistical learning book The Elements of Statistical Learning. Very good illustrations and ...             Probability Theory: The Logic of Science17 reviews E. T. Jaynes Cambridge University Press, 2003 unbelievably charming and intelligent + Thought provoking + Flawed gems + On first reading + Great hard to find information             Learning Bayesian Networks (Artificial Intelligence)2 reviews Richard E. Neapolitan Prentice Hall, 2003 An excellent overview + Enjoying this book enormously In just a decade, Bayesian networks have went from being a mere academic curiosity to a highly useful field with myriads of applications. Indeed, the applications of Bayesian networks are wide-ranging and include disparate fields such as network engineering, bioinformatics, medical diagnostics, and ...             Introduction to Graphical Modelling2 reviews David Edwards Springer, 2000 this is about directed graphs not graphics + directed graphs, path analysis and causality not the common statistical graphics Because graphic methods are very popular in statistics, when you read the title you might think this is a book on the use of graphics in statistics. That is not what the book is about. The directed graph on the cover might be a hint for some. The book deals with the theory of undirected and ...             Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) The MIT Press, 2007 Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In Introduction to Statistical Relational Learning, ...             Bayesian Networks and Decision Graphs (Information Science and Statistics)8 reviews Finn V. Jensen, Thomas D. Nielsen Springer, 2007 Good Book + A very good introduction to Bayesian networks + Accessible introduction to Bayesian Networks For an introduction to the subject, this book is unequivocal in my experience with the literature. Great read that has propelled me forward into combining a bayesian network with a physical model to approach a very complex sediment transport problem.             Learning in Graphical Models (Adaptive Computation and Machine Learning)2 reviews The MIT Press, 1998 Simply Superb... + Recommended, but not the place to begin My area of research revolves around graphical models... Best Book... The book that introduced me as to how effective graphical models are... As stated in the editorial review, graphical model is the marriage between graph theory and probability and this book justifies the sacredness of this ...             Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference7 reviews Judea Pearl Morgan Kaufmann, 1988 Outstanding introduction to the field + a classic book + Elegant Discussion On Probabilistic Reasoning And Uncertainty Recently I needed to learn the principles of Bayesian networks quickly, so I bought three books: this one by Pearl, "Pattern Recognition and Machine Learning" by Bishop, and "Bayesian Artificial Intelligence" by Korb and Nicholson. Each has a very different audience and different set of strengths. ...             Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and ...2 reviews Martin L. Puterman Wiley-Interscience, 2005 From the author of Approximate Dynamic Programming + Excellent and detailed, although focusing on exact algorithms only For anyone looking for an introduction to classic discrete state, discrete action Markov decision processes this is the last in a long line of books on this theory, and the only book you will need. The presentation covers this elegant theory very thoroughly, including all the major problem classes ...             Information Theory, Inference & Learning Algorithms8 reviews David J. C. MacKay Cambridge University Press, 2002 A must have... + pretty much indispensible + Outstanding book, especially for statisticians + Great wish it had more n option inverse problems + Great Book As Far As It Goes