{"product_id":"bayesian-networks-9780470743041","title":"Bayesian Networks","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks  is a self-contained introduction to the theory and applications of Bayesian networks.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"It assumes only a basic knowledge of probability, statistics and mathematics and is well suited for classroom teaching . . . Each chapter of the book is concluded with short notes on the literature and a set of helpful exercises.\" (Mathematical Reviews, 2011)\u003cbr\u003e \u003cbr\u003e   \u003cp\u003e\"Extensively tested in classroom teaching … .The authors clearly define all concepts and provide numerous examples and exercises.\" (\u003ci\u003eBook News\u003c\/i\u003e, December 2009)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Graphical models and probabilistic reasoning\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 Axioms of probability and basic notations.\u003c\/p\u003e \u003cp\u003e1.3 The Bayes update of probability.\u003c\/p\u003e \u003cp\u003e1.4 Inductive learning.\u003c\/p\u003e \u003cp\u003e1.5 Interpretations of probability and Bayesian networks.\u003c\/p\u003e \u003cp\u003e1.6 Learning as inference about parameters.\u003c\/p\u003e \u003cp\u003e1.7 Bayesian statistical inference.\u003c\/p\u003e \u003cp\u003e1.8 Tossing a thumb-tack.\u003c\/p\u003e \u003cp\u003e1.9 Multinomial sampling and the Dirichlet integral.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Probabilistic theories of causality, Bayes’ rule, multinomial sampling and the Dirichlet density.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Conditional independence, graphs and\u003c\/b\u003e \u003cb\u003e\u003ci\u003ed\u003c\/i\u003e-separation\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e2.1 Joint probabilities.\u003c\/p\u003e \u003cp\u003e2.2 Conditional independence.\u003c\/p\u003e \u003cp\u003e2.3 Directed acyclic graphs and \u003ci\u003ed\u003c\/i\u003e-separation.\u003c\/p\u003e \u003cp\u003e2.4 The Bayes ball.\u003c\/p\u003e \u003cp\u003e2.5 Potentials.\u003c\/p\u003e \u003cp\u003e2.6 Bayesian networks.\u003c\/p\u003e \u003cp\u003e2.7 Object oriented Bayesian networks.\u003c\/p\u003e \u003cp\u003e2.8 \u003ci\u003ed\u003c\/i\u003e-Separation and conditional independence.\u003c\/p\u003e \u003cp\u003e2.9 Markov models and Bayesian networks.\u003c\/p\u003e \u003cp\u003e2.10 \u003ci\u003eI\u003c\/i\u003e-maps and Markov equivalence.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Conditional independence and \u003ci\u003ed\u003c\/i\u003e-separation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Evidence, sufficiency and Monte Carlo methods\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e3.1 Hard evidence.\u003c\/p\u003e \u003cp\u003e3.2 Soft evidence and virtual evidence.\u003c\/p\u003e \u003cp\u003e3.3 Queries in probabilistic inference.\u003c\/p\u003e \u003cp\u003e3.4 Bucket elimination.\u003c\/p\u003e \u003cp\u003e3.5 Bayesian sufficient statistics and prediction sufficiency.\u003c\/p\u003e \u003cp\u003e3.6 Time variables.\u003c\/p\u003e \u003cp\u003e3.7 A brief introduction to Markov chain Monte Carlo methods.\u003c\/p\u003e \u003cp\u003e3.8 The one-dimensional discrete Metropolis algorithm.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Evidence, sufficiency and Monte Carlo methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Decomposable graphs and chain graphs\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e4.1 Definitions and notations.\u003c\/p\u003e \u003cp\u003e4.2 Decomposable graphs and triangulation of graphs.\u003c\/p\u003e \u003cp\u003e4.3 Junction trees.\u003c\/p\u003e \u003cp\u003e4.4 Markov equivalence.\u003c\/p\u003e \u003cp\u003e4.5 Markov equivalence, the essential graph and chain graphs.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Decomposable graphs and chain graphs.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Learning the conditional probability potentials\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e5.1 Initial illustration: maximum likelihood estimate for a fork connection.\u003c\/p\u003e \u003cp\u003e5.2 The maximum likelihood estimator for multinomial sampling.\u003c\/p\u003e \u003cp\u003e5.3 MLE for the parameters in a DAG: the general setting.\u003c\/p\u003e \u003cp\u003e5.4 Updating, missing data, fractional updating.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Learning the conditional probability potentials.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Learning the graph structure\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e6.1 Assigning a probability distribution to the graph structure.\u003c\/p\u003e \u003cp\u003e6.2 Markov equivalence and consistency.\u003c\/p\u003e \u003cp\u003e6.3 Reducing the size of the search.\u003c\/p\u003e \u003cp\u003e6.4 Monte Carlo methods for locating the graph structure.\u003c\/p\u003e \u003cp\u003e6.5 Women in mathematics.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Learning the graph structure.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Parameters and sensitivity\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e7.1 Changing parameters in a network.\u003c\/p\u003e \u003cp\u003e7.2 Measures of divergence between probability distributions.\u003c\/p\u003e \u003cp\u003e7.3 The Chan-Darwiche distance measure.\u003c\/p\u003e \u003cp\u003e7.4 Parameter changes to satisfy query constraints.\u003c\/p\u003e \u003cp\u003e7.5 The sensitivity of queries to parameter changes.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Parameters and sensitivity.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Graphical models and exponential families\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e8.1 Introduction to exponential families.\u003c\/p\u003e \u003cp\u003e8.2 Standard examples of exponential families.\u003c\/p\u003e \u003cp\u003e8.3 Graphical models and exponential families.\u003c\/p\u003e \u003cp\u003e8.4 Noisy ‘or’ as an exponential family.\u003c\/p\u003e \u003cp\u003e8.5 Properties of the log partition function.\u003c\/p\u003e \u003cp\u003e8.6 Fenchel Legendre conjugate.\u003c\/p\u003e \u003cp\u003e8.7 Kullback-Leibler divergence.\u003c\/p\u003e \u003cp\u003e8.8 Mean field theory.\u003c\/p\u003e \u003cp\u003e8.9 Conditional Gaussian distributions.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Graphical models and exponential families.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Causality and intervention calculus\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Conditioning by observation and by intervention.\u003c\/p\u003e \u003cp\u003e9.3 The intervention calculus for a Bayesian network.\u003c\/p\u003e \u003cp\u003e9.4 Properties of intervention calculus.\u003c\/p\u003e \u003cp\u003e9.5 Transformations of probability.\u003c\/p\u003e \u003cp\u003e9.6 A note on the order of ‘see’ and ‘do’ conditioning.\u003c\/p\u003e \u003cp\u003e9.7 The ‘Sure Thing’ principle.\u003c\/p\u003e \u003cp\u003e9.8 Back door criterion, confounding and identifiability.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: Causality and intervention calculus.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The junction tree and probability updating\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e10.1 Probability updating using a junction tree.\u003c\/p\u003e \u003cp\u003e10.2 Potentials and the distributive law.\u003c\/p\u003e \u003cp\u003e10.3 Elimination and domain graphs.\u003c\/p\u003e \u003cp\u003e10.4 Factorization along an undirected graph.\u003c\/p\u003e \u003cp\u003e10.5 Factorizing along a junction tree.\u003c\/p\u003e \u003cp\u003e10.6 Local computation on junction trees.\u003c\/p\u003e \u003cp\u003e10.7 Schedules.\u003c\/p\u003e \u003cp\u003e10.8 Local and global consistency.\u003c\/p\u003e \u003cp\u003e10.9 Message passing for conditional Gaussian distributions.\u003c\/p\u003e \u003cp\u003e10.10 Using a junction tree with virtual evidence and soft evidence.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercises: The junction tree and probability updating.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Factor graphs and the sum product algorithm\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e11.1 Factorization and local potentials.\u003c\/p\u003e \u003cp\u003e11.2 The sum product algorithm.\u003c\/p\u003e \u003cp\u003e11.3 Detailed illustration of the algorithm.\u003c\/p\u003e \u003cp\u003eNotes.\u003c\/p\u003e \u003cp\u003eExercise: Factor graphs and the sum product algorithm.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864638959959,"sku":"9780470743041","price":71.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470743041.jpg?v=1722272845","url":"https:\/\/bookcurl.com\/products\/bayesian-networks-9780470743041","provider":"Book Curl","version":"1.0","type":"link"}