Bayesian Inference

This book offers a comprehensive reference guide to neutrosophic theory and its applications in decision-making in numerous disciplines, ranging fr...

This book focuses on the statistical modeling of geophysical and environmental data using Bayesian latent Gaussian models. The structure of these m...

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling br...

The implementation problem lies at the heart of a theory of institutions. Simply stated, the aim of implementation theory is to investigate in a ri...

The second edition of Empirical Bayes Methods details are provided of the derivation and the performance of empirical Bayes rules for a variety of ...

A thorough grounding in Markov chains and martingales is essential in dealing with many problems in applied probability, and is a gateway to the mo...

Reliability and maintenance modeling with optimization is the most fundamental and interdisciplinary research area that can be applied to every tec...

Based on the authors lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage of statistical infer...

This book is about the interplay between chance and order, but limited to mostly binary events, such as success/failure as they occur in a diversit...

This textbook will continue to be the best suitable textbook written specifically for a first course on probability theory and designed for industr...

This book explains the importance of using the probability that the hypothesis is correct (PHC), an intuitive measure that anyone can understand, a...

This book introduces the concept of bespoke learning, a new mechanistic approach that makes it possible to generate values of an output variable at...

There has been a tremendous increase in the breadth of application of Bayesian methods to problems in industry in the last twenty or so years. Howe...

This book examines statistical models for frequency data.  The primary focus is on log-linear models for contingency tables but also includes exten...

This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the e...

This book offers a comprehensive overview of statistical methodology for modelling and evaluating spatial variables useful in a variety of applicat...

The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in...

We live in the era of big data. However, small data sets are still common for ethical, financial, or practical reasons. Small sample sizes can caus...

We live in the era of big data. However, small data sets are still common for ethical, financial, or practical reasons. Small sample sizes can caus...

Uncertainty quantification is an important step in establishing the predictive accuracy of simulation models employed in a broad range of disciplin...