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Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. < give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book. Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark. Thomas D. Nielsen is an associate professor at the same department.

Mathematical statistics --- Computer Science. --- Probability and Statistics in Computer Science. --- Artificial Intelligence (incl. Robotics). --- Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences. --- Computer science. --- Artificial intelligence. --- Statistics. --- Informatique --- Intelligence artificielle --- Statistique --- Bayesian statistical decision theory --- Machine Learning --- Neural networks (Computer science) --- Decision Making --- Data processing --- Bayesian statistical decision theory -- Data processing. --- Decision making. --- Electronic books. -- local. --- Machine learning. --- Neural networks (Computer science). --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Machine learning --- Decision making --- Data processing. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Learning, Machine --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Bayes' solution --- Bayesian analysis --- Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- 681.3*I28 Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- Statistique bayésienne --- Life sciences. --- Mathematical statistics. --- Plant science. --- Botany. --- Probabilities. --- Life Sciences. --- Plant Sciences. --- Probability Theory and Stochastic Processes. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- 519.2 --- 681.3*I28 --- Artificial intelligence --- Natural computation --- Soft computing --- Machine theory --- Choice (Psychology) --- Problem solving --- Statistical decision --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- Botanical science --- Phytobiology --- Phytography --- Phytology --- Plant biology --- Plant science --- Biology --- Natural history --- Plants --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Biosciences --- Sciences, Life --- Science --- Apprentissage automatique --- Réseaux neuronaux (Informatique) --- Prise de décision --- Distribution (Probability theory. --- Artificial Intelligence. --- Informatics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Prise de décision. --- Apprentissage automatique. --- Informatique. --- Statistics . --- Floristic botany --- Bayesian statistical decision theory - Data processing