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Pattern Recognition and Machine Learning
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, …
Feedforward Neural Network Methodology
The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two …
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural …
Estimation of Dependences Based on Empirical Data
Twenty-?ve years have passed since the publication of the Russian version of the book Estimation of Dependencies Based on Empirical Data (EDBED for short). Twen- ?ve years is a …
The Cross-Entropy Method
This book is a comprehensive and accessible introduction to the cross-entropy (CE) method. The CE method started life around 1997 when the first author proposed an adaptive …
Sequential Monte Carlo Methods in Practice
The advent of cheap and massive computational power in conjunction with developments in applied statistics have stimulated many advancements in the field of sequential Monte Carlo …
Nonlinear Dimensionality Reduction
Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and …
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, …
Cumulative Sum Charts and Charting for Quality Improvement
Cumulative sum (CUSUM) control charting is a valuable tool for detecting and diagnosing persistent shifts in series of readings. It is used in traditional statistical process …
The Nature of Statistical Learning Theory
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of …
Probabilistic Networks and Expert Systems
WINNER OF THE 2001 DEGROOT PRIZE! Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while …
Computer Intrusion Detection and Network Monitoring
In the fall of 1999, I was asked to teach a course on computer intrusion detection for the Department of Mathematical Sciences of The Johns Hopkins University. That course was the …