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Information Science and Statistics

Information Science and Statistics
21 Jahrgänge | 1998 - 2013


The fields of computer science, communications, probability and statistics have become increasingly intertwined. Two significant trends characterize this development: algorithmic issues arising throughout probability and statistics, and probabilistic methods playing increasingly important roles in the design and analysis of information systems.

This series will feature graduate textbooks and advanced monographs that involve the integration of algorithmic and probabilistic or statistical aspects. The series will include books on fundamental theoretical issues and more applied books that present new algorithms and architectures of general interest. Suitable topics include randomized algorithms and combinatorics, graphical models, machine learning, algorithmic game theory, source coding and error control coding, network information theory, simulation, rare events, distributed and robust optimization, networking, information retrieval, information management, speech processing, statistical natural language processing, computer vision, and robotics.

Alle Bücher der Reihe Information Science and Statistics

2013 | Buch

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, and analyze intelligent systems for decision support based o

2011 | Buch

Statistical Image Processing and Multidimensional Modeling

Images are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a stati

2010 | Buch

Information Theoretic Learning

Renyi's Entropy and Kernel Perspectives

2010 | Buch

Computational Methods in Biometric Authentication

Statistical Methods for Performance Evaluation

Biometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everythin

2008 | Buch

Bayesian Networks and Influence Diagrams

A Guide to Construction and Analysis

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, p

2008 | Buch

Support Vector Machines

Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and pred

2007 | Buch

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 classical metric multidimensional scaling suffer from being based on linear

2007 | Buch

Bayesian Networks and Decision Graphs

February 8, 2007

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 c

2007 | Buch

Information and Complexity in Statistical Modeling

No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the informatio

2006 | Buch

Estimation of Dependences Based on Empirical Data

Empirical Inference Science Afterword of 2006

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 long period of time. During these years many things have happened. Lookin

2005 | Buch

Statistical and Inductive Inference by Minimum Message Length

Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. Mywork

2005 | Buch

On Probabilistic Conditional Independence Structures

Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic appr

2004 | Buch

The Cross-Entropy Method

A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning

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 algorithm for rare-event simulation using a cross-entropy minimization …

2001 | Buch

Sequential Monte Carlo Methods in Practice

Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo …

2001 | Buch

Computer Intrusion Detection and Network Monitoring

A Statistical Viewpoint

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 genesis of this book. I had been working in the field for several …

2001 | Buch

Bayesian Networks and Decision Graphs

Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and to …

2000 | Buch

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 function estimation based on empirical data. Omitting proofs and …

1999 | Buch

Probabilistic Networks and Expert Systems


Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the …

1999 | Buch

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 factors have cooperated to at last provide systems engineers and …

1999 | Buch

The Practice of Time Series Analysis

Due to the introduction of the information criterion AIC and development of prac­ tical use of Bayesian modeling, the method of time analysis is now showing remarkable progress. In attempting the study of a new field the actual phenomenon is …