Bayesian inference Books

Developed from the author's course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations...

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary...

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the...

Improvements in safety in the air and in space can be achieved through better ergonomics, better work environments, and other efforts of traditional avionic psychology that directly affect human behaviors and performance. Not limited to just the...

This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work...

Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide...

Based on the author's 20 years of teaching, Risk Analysis in Engineering: Techniques, Tools, and Trends presents an engineering approach to probabilistic risk analysis (PRA). It emphasizes methods for comprehensive PRA studies, including techniques for...

Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the...

The book is based on the model-based theory, used widely by scientists in many fields. It covers simple experimental and survey designs, and　probability models up to and including generalised linear (regression) models and some extensions of these,...

A guide to implementing and operating a practical reliability program using carefully designed experiments to provide information quickly, efficiently and cost effectively. It emphasizes real world solutions to daily problems. The second edition contains...

Outside of randomized experiments, association does not imply causation, and yet there is nothing defective about our knowledge that smoking causes lung cancer, a conclusion reached in the absence of randomized experimentation with humans. How is that...

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology...

This book provides an introductory account of the mathematical analysis of stochastic processes. It is helpful for statisticians and applied mathematicians interested in methods for solving particular problems, rather than for pure mathematicians...

Introduction to Stochastic Level Crossing Techniques describes stochastic models and their analysis using the System Point Level Crossing method (abbreviated SPLC or LC). This involves deriving probability density functions (pdfs) or cumulative...

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied...

This book provides real-life examples and illustrations of models in reliability engineering and statistical quality control and establishes a connection between the theoretical framework and their engineering applications.

With the boom of big data and machine learning and the subsequent need for parallel processing technologies, fork-join queues are more relevant now than ever before. In this book, new estimates of the average response time in fork-join queues are...

Stochastic modeling is a set of quantitative techniques for analyzing practical systems with random factors. This area is highly technical and mainly developed by mathematicians. Most existing books are for those with extensive mathematical training;...

This volume introduces the theoretical ideas in probability and statistics by means of examples. The strengths of the BASIC computer language are exploited to illustrate probabilistic and statistical ideas. Topics described by the Committee on the...

This book discusses Change Management Impact Analysis and how this method is used to analysis the risks and benefits of a change management initiative when it pertains to obtaining critical insight into how the change management program budge should be...

Mathematics of Keno and Lotteries is an elementary treatment of the mathematics, primarily probability and simple combinatorics, involved in lotteries and keno. Keno has a long history as a high-advantage, high-payoff casino game, and state lottery games...

Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct...

In response to unanswered difficulties in the generalized case of conditional expectation and to treat the topic in a well-deservedly thorough manner, M.M. Rao gave us the highly successful first edition of Conditional Measures and Applications. Until...

So much to read, so little time? This brief overview of The Signal and the Noise tells you what you need to know—before or after you read Nate Silver’s book.Crafted and edited with care, Worth Books set the standard for quality and give you...

In this monograph the narrow topology on random probability measures on Polish spaces is investigated in a thorough and comprehensive way. As a special feature, no additional assumptions on the probability space in the background, such as completeness or...

This concise, easy-to-follow book stimulates interest and develops proficiency in statistical analysis. Packed full of helpful exercises-covering a wide variety of conditions,random patterns, and simple models-Understanding Randomness presents a logical...

Probability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most...

With the collapse of Demographic Transition Theory, new theories of population must not just be explanations, but should be falsifiable theories which can compute the number of occurrences of marriages and births. This book reviews computable marriage...

This book provides a comprehensive survey of the most recent methodology--both theoretical and applied--on the statistical analysis and detection of defective/"non-conforming" items in various types of inspection for attributes, when the inspection...

Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have...

In many areas of applied statistics like economics or finance it is often desirable to find groups of similar time series in a set or panel of time series. Therefore, clustering techniques are required to determine subsets of similar time series. While...

This essential text explores the Bayesian analysis of independent multi-normal processes when neither the mean nor the precision is known. Written by two of the most renowned mathematicians in the field, this book offers readers a comprehensive and...

Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of...

Sojourns and Extremes of Stochastic Processes is a research monograph in the area of probability theory. During the past thirty years Berman has made many contributions to the theory of the extreme values and sojourn times of the sample functions of...

Provides an introduction to decision analysis. This book is based upon a number of papers and articles taken from the Operational Research Society's journal and other publications. However, the book is not simply a 'collection of reprints': Professor...

Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of...

Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory,...