Bayesian inference Books

This book provides one of the first unified accounts of the dynamic aspects involved in the strategic behavior in queues. The author presents the material in an easy style with mostly non-technical language, emphasizing models and qualitative insights...

The aim of Understanding Analytic Hierarchy Process book is to provide the reader with a critical guide to AHP. In this book, the AHP method is considered primarily as a mathematical technique supporting the decision-making process. 

Written by pioneers in an exciting new field, this book introduces the application of polynomial algebra to experimental design, discrete probability, and statistics. It begins with an introduction to Grobner bases and a thorough description of their...

Incorporating a collection of recent results, Pólya Urn Models deals with discrete probability through the modern and evolving urn theory and its numerous applications. It looks at how some classical problems of discrete probability have roots in urn...

This book should serve as an invaluable and fascinating resource for university, college, and high school students who wish to extend their reading, as well as for teachers and lecturers who want to liven up their courses while retaining academic rigour.

Bayesian methods in reliability cannot be fully utilized without understanding the essential differences that exist between frequentist probability and subjective probability. Switching from the frequentist to the subjective approach requires that some...

Random sets as models for set-valued observations, are a new type of data proving useful in areas such as survey sampling, biostatistics, and intelligent systems. This is the first text to explore the topic in depth, using extended probability theory to...

Based on the authors' lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference...

At the International Indian Statistical Association Conference, held at McMaster University in Ontario, Canada, participants focused on advancements in theory and methodology of probability and statistics. This is one of two volumes containing invited...

Neste livro, são realizados exercícios sobre os seguintes tópicos matemáticos:teoria da estimativateste e verificação de hipótesesregressão linearTambém são apresentadas dicas...

Artificial Intelligence and other Intelligent Systems heavily contribute to evolution and growth in the field of Operations Management. Intelligent Systems in Operations: Models, Methods, and Applications introduces current and original research in...

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features:- Conduct Bayesian data analysis with...

Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial...

Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject...

In real-life scenarios, service management involves complex decision-making processes usually affected by random or stochastic variables. Under such uncertain conditions, the development and use of robust and flexible strategies, algorithms, and methods...

Winner of the IIE Book of the Month for June 2012A project can be simple or complex. In each case, proven project management processes must be followed. In all cases of project management implementation, control must be exercised in order to assure that...

Based on a popular course taught by the late Gian-Carlo Rota of MIT, with many new topics covered as well, Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural...

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging...

This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate...

FeaturesWorked examples from the social sciencesClearly written, without mathematical formulaeWell-structured, for beginnersComprehensive chapter on categorical analysis - not just 'Chi squared'Effect sizes and confidence intervalsIntroduces Bayesian...

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent...

This book explains the misuses and abuses of Null Hypothesis Significance Tests, which are reconsidered in light of Jeffreys' Bayesian concept of the role of statistical inference, in experimental investigations. Minimizing the technical aspects, the...

This book introduces the concept of "bespoke learning", a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides...

This book provides an overview of the emerging topics in biostatistical theories and methods through their applications to evidence-based global health research and decision-making. It brings together some of the top scholars engaged in biostatistical...

Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real...

Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited...

Although power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years, it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf)...

Written with students and professors in mind, Analysis of Queues: Methods and Applications combines coverage of classical queueing theory with recent advances in studying stochastic networks. Exploring a broad range of applications, the book contains...

Reliability and maintenance modeling with optimization is the most fundamental and interdisciplinary research area that can be applied to every technical and management field. Reliability and Maintenance Modeling with Optimization: Advances and...

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model fitting, estimation procedures, and computing issues. The exponential growth in computing power over the last two decades has revolutionized...

This volume comprises selected papers presented at the 12th Winter School on Stochastic Processes and their Applications, which was held in Siegmundsburg, Germany, in March 2000. The contents include Backward Stochastic Differential Equations; Semilinear...

Collecting information previously scattered throughout the vast literature, including the author's own research, Stochastic Relations: Foundations for Markov Transition Systems develops the theory of stochastic relations as a basis for Markov transition...

The Statistics of Poker by Kuro Aksara is a comprehensive and practical textbook that explores the intersection of statistics and poker, covering everything from basic probability to advanced statistical modeling. Whether you're a beginner looking to...

2020 Taylor & Francis Award Winner for Outstanding New Textbook!Featuring recent advances in the field, this new textbook presents probability and statistics, and their applications in stochastic processes. This book presents key information for...

An introduction to general theories of stochastic processes and modern martingale theory. The volume focuses on consistency, stability and contractivity under geometric invariance in numerical analysis, and discusses problems related to implementation,...

This book presents Seymour Geisser's views on predictive or observable inference and its advantages over parametric inference. It focuses on the predictive applications of the Bayesian approach. The book also presents predictive analyses that have no...

This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering.The first chapter contains a summary of basic probability theory. Chapters two to five deal...

"Multivariate Analysis: Beyond the Surface" provides an in-depth exploration of advanced multivariate techniques, as well as a thorough introduction to the fundamental principles and concepts of multivariate analysis.The book present a wide...

Measure Theory and Probability: An Entertaining Journey Through the World of Math Measure Theory and Probability: An Entertaining Journey Through the World of Math Measure Theory and Probability: An Entertaining Journey Through the World of Math Measure...

This textbook will continue to be the best suitable textbook written specifically for a first course on probability theory and designed for industrial engineering and operations management students. The book offers theory in an accessible manner and...

This gives a comprehensive introduction to the (standard) statistical analysis based on the theory of martingales and develops entropy methods in order to treat dependent data in the framework of martingales. The author starts a summary of the martingale...

Mathematical statistics typically represents one of the most difficult challenges in statistics, particularly for those with more applied, rather than mathematical, interests and backgrounds. Most textbooks on the subject provide little or no review of...

Presents modern Bayesian statistical models and methods for identifying treatments tailored to individual patients using their prognostic variables and predictive biomarkers.

This book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view...

How can major corporations and governments more quickly and accurately detect and address cyberattacks on their networks? How can local authorities improve early detection and prevention of epidemics? How can researchers improve the identification and...

Inhomogeneous Random Evolutions and Their Applications explains how to model various dynamical systems in finance and insurance with non-homogeneous in time characteristics. It includes modeling for:financial underlying and derivatives via Levy processes...