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You are currently using the site but have requested a page in the site. Would you like to change to the site? Timo Koski , John Noble. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology.
Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest. John M. Permissions Request permission to reuse content from this site. The authors clearly define all concepts and provide numerous examples and exercises. Wiley Series in Probability and Statistics. Undetected location. NO YES. Bayesian Networks: An Introduction.
Selected type: Hardcover. Added to Your Shopping Cart. View on Wiley Online Library. This is a dummy description. Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics.
All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications.
A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. Table of contents Preface. Exercises: Conditional independence and d -separation. Exercises: Evidence, sufficiency and Monte Carlo methods.
Exercises: Decomposable graphs and chain graphs. Exercises: Learning the conditional probability potentials. Exercises: Learning the graph structure. Exercises: Parameters and sensitivity. Exercises: Graphical models and exponential families. Exercises: Causality and intervention calculus. Exercises: The junction tree and probability updating. Exercise: Factor graphs and the sum product algorithm. Reviews "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.
Bayesian Networks: An Introduction
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Bayesian Networks : An Introduction
This book will prove a valuable resource for postgraduatestudents of statistics, computer engineering, mathematics, datamining, artificial intelligence, and biology. Researchers and users of comparable modelling or statisticaltechniques such as neural networks will also find this book ofinterest. Bayesian Networks : An Introduction. Timo Koski , John Noble.