Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian NetworksSpringer Science & Business Media, 29.5.2006 - 324 sivua Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback. |
Sisältö
1 | |
Building and Using Probabilistic Networks | 25 |
Graph Theory 43 | 42 |
Markov Properties on Graphs | 63 |
Discrete Networks 83 | 82 |
Gaussian and Mixed DiscreteGaussian Networks | 125 |
Checking Models Again | 225 |
4 | 234 |
Structural Learning | 242 |
6 | 256 |
Epilogue | 265 |
B Gibbs Sampling | 271 |
Author Index 307 | 306 |
319 | |
Muita painoksia - Näytä kaikki
Probabilistic Networks and Expert Systems: Exact Computational Methods for ... Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter Esikatselu ei käytettävissä - 2003 |
Probabilistic Networks and Expert Systems: Exact Computational Methods for ... Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter Esikatselu ei käytettävissä - 2007 |
Yleiset termit ja lausekkeet
algorithm analysis ancestral set approximation Artificial Intelligence Bayes Bayesian networks Birth asphyxia calculate CG potentials chain component chain graph Chapter chordal chordal graph COLLECT EVIDENCE complete data computation conditional distribution conditional independence conditional probability conditional probability tables Cowell Dawid decision network decision problems decision sequence decomposable graphs defined Definition denote density described directed acyclic graph directed Markov Dirichlet Dirichlet distribution disease edges efficient elimination set elimination tree example expert systems factorization Figure find finding first fixed flow Gibbs sampling given graph Q graphical models incomplete inference initial Jensen joint distribution junction tree learning likelihood marginal likelihood Markov chain Markov property methods mixture monitors moral graph neighbours node observed parameters parents probabilistic expert systems probabilistic networks probability distribution probable configuration propagation random variables representation result Section separator Shenoy specification Spiegelhalter Statistical structure subset Theorem theory tion triangulated undirected graph updating utility values vertex vertices