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Character Evidence : An Abductive Theory
This book is on evidence for character judgments, This book answers the question using a model of abductive reasoning, commonly called inference to the best explanation. The methodology of the book derives from recent work on models of reasoning in argumentation theory and artificial intelligence. The aim is not just to show how character judgments are made, but to show how they should be properly be made based on sound reasoning, in order to avoid errors and superficial judgments of a kind that are common.
Bayesian core : A practical approach to computational Bayesian statistics
This Bayesian modeling book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
Bayesian computation with R : Introduces Bayesian modeling by use of computation using the R language
R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language.
Applied Statistics Using SPSS, STATISTICA, MATLAB and R
The book provides a comprehensive coverage of the main statistical analysis topics important for practical applications such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics.
An Introduction to Queueing Theory : Modeling and Analysis in Applications
This introductory textbook is designed for a one-semester course on queueing theory that does not require a course in stochastic processes as a prerequisite. By integrating the necessary background on stochastic processes with the analysis of models, the work provides a sound foundational introduction to the modeling and analysis of queueing systems for a broad interdisciplinary audience of students in mathematics, statistics, and applied disciplines such as computer science, operations research, and engineering.
An Introduction to Bayesian Analysis : Theory and Methods
This book is a contemporary introduction to theory, methods and computation in Bayesian Analysis. It focuses on topics that have stood the test of time and on emerging areas. No other such book is available in the market.
All of Nonparametric Statistics
The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods.This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory.
Advances in Ranking and Selection, Multiple Comparisons, and Reliability: Methodology and Applications
S. Panchapakesan has made significant contributions to ranking and selection and has published in many other areas of statistics, including order statistics, reliability theory, stochastic inequalities, and inference. Written in his honor, the twenty invited articles in this volume reflect recent advances in these fields and form a tribute to Panchapakesan’s influence and impact on these areas. Thematically organized, the chapters cover a broad range of topics from: Inference / Ranking and Selection / Multiple Comparisons and Tests / Agreement Assessment / Reliability / Biostatistics
Advances in Probabilistic Graphical Models
This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.
Advances in Mathematical and Statistical Modeling
Enrique Castillo is a leading figure in several mathematical, statistical, and engineering fields, having contributed seminal work in such areas as statistical modeling, extreme value analysis, multivariate distribution theory, Bayesian networks, neural networks, functional equations, artificial intelligence, linear algebra, optimization methods, numerical methods, reliability engineering, as well as sensitivity analysis and its applications. Organized to honor Castillo's significant contributions, this volume is an outgrowth of the International Conference on Mathematical and Statistical Modeling and covers recent advances in the field. Also presented are applications to safety, reliability and life-testing, financial modeling, quality control, general inference, as well as neural networks and computational techniques.
Advances in Distribution Theory, Order Statistics, and Inference
Barry Arnold has made fundamental contributions to many different areas of statistics, including distribution theory, Bayesian inference, multivariate analysis, bounds and orderings, and characterization problems. Organized to honor Arnold’s significant contributions to the field, this volume is an outgrowth of the "International Conference on Distribution Theory, Order Statistics, and Inference," held at the University of Cantabria, Santander, Spain.Several distinguished and active researchers highlight some of the recent developments in statistical distribution theory, order statistics and their properties, as well as inferential methods associated with them. Applications to survival analysis, reliability, quality control, and environmental problems are emphasized.
Advances and Innovations in Systems, Computing Sciences and Software Engineering
Acollection of world class paper articles addressing the following topics: Image and Pattern Recognition: Compression, Image processing, Signal Processing Architectures, Signal Processing for Communication, Signal Processing Implementation, Speech Compression, and Video Coding Architectures. Languages and Systems: Algorithms, Databases, Embedded Systems and Applications, File Systems and I/O, Geographical Information Systems, Kernel and OS Structures, Knowledge Based Systems, Modeling and Simulation, Object Based Software Engineering, Programming Languages, and Programming Models and tools. Parallel Processing: Distributed Scheduling, Multiprocessing, Real-time Systems, Simulation Modeling and Development, and Web Applications. New trends in computing: Computers for People of Special Needs, Fuzzy Inference, Human Computer Interaction, Incremental Learning, Internet-based Computing Models, Machine Intelligence, Natural Language Processing, Neural Networks, and Online Decision Support System.
Advanced Fuzzy Logic Technologies in Industrial Applications
Addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs. The tuning process is a major focus in this volume because it is the most difficult stage in fuzzy control application. Using new methods such as µ-law technique, histogram equalization and the Bezier-based method, all detailed here, the tuning process can be significantly simplified and control performance improved. The other great strength of this book lies in the range and contemporaneity of its applications and examples which include: laser tracking and control; robot calibration; image processing and pattern recognition; medical engineering; audio systems; autonomous underwater vehicles and data mining.
A History of Parametric Statistical Inference from Bernoulli to Fischer, 1713-1935
This is a history of parametric statistical inference, written by one of the most important historians of statistics of the 20th century, Anders Hald. This book can be viewed as a follow-up to his two most recent books, although this current text is much more streamlined and contains new analysis of many ideas and developments. And unlike his other books, which were encyclopedic by nature, this book can be used for a course on the topic, the only prerequisites being a basic course in probability and statistics.
