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Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data All basic concepts carefully explained and illustrated by examples throughout Contains background material including graphical representation, including Markov and Bayesian Networks. Includes a comprehensive bibliography.

Detailed examples will be provided ranging from the very basic to the more advanced; they will also reflect realistic data sets (available from the Internet). An underlying emphasis is given to Generalized Linear Models (GLMs) that are familiar to most readers and researchers.

This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.

The first book to present a unified account of symbolic data analysis methods in a consistent statistical framework, Symbolic Data Analysis features a substantial number of examples from a range of application areas, including health, the social sciences, economics, and computer science.

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems.

* Presents the latest developments in Monte Carlo research. * Provides a toolkit for simulating complex systems using MCMC. * Introduces a wide range of algorithms including Gibbs sampler, Metropolis-Hastings and an overview of sequential Monte Carlo algorithms.

A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models.

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such a data.

Covering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate exploration of data.

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge.

* Provides a general framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification. * The proposed methods are applied to different real data sets to demonstrate their ability.

Retaining the general organization and style of its predecessor, this new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing and computational statistics.