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Ng Kai Wang: The Converse of Bayes
Theorem with Applications

The Converse of Bayes Theorem with Applications

The book comprises original articles on topical issues in risk theory, rational decision making, statistical decisions, and control of stochastic systems.

Modern Optimization Methods for Decision Making Under Risk and Uncertainty

Meta-analysis Using R

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Intended to provide a nearly comprehensive account of methods, theory, applications, as well as open problems related to robust SAE, that the monograph will help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods.

Methods, Theory, Applications, and Open Problems

Robust Small Area Estimation

Theory and Practice

Business Analytics

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.

Likelihood and its Extensions

There has been a lot of innovation in systems engineering as well as some fundamental advances in the field of optics, imaging, lasers, and photonics that warrant attention. This volume focuses on applications, tools, and techniques of systems engineering-related topics from government, industrial, and academic settings.

Applications, Tools, and Techniques

Handbook of Systems Engineering and Analysis of Electro-Optical and Infrared Systems

Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

This advanced textbook goes beyond the current limits of item response modeling to enhance understanding and help readers be more creative in the development and usage of the models. The book first provides a basic survey of the approach and then lays out a large space of important models and their applications. It finishes with information about using different computational approaches to estimating the models, including aspects of testing, as well as several appendices containing helpful codes and examples.

This advanced textbook goes beyond the current limits of item response modeling to enhance understanding and help readers be more creative in the development and usage of the models. The book first provides a basic survey of the approach and then lays out a large space of important models and their applications. It finishes with information about using different computational approaches to estimating the models, including aspects of testing, as well as several appendices containing helpful codes and examples.

An Explanatory Approach

A Generalized Framework for Item Response Models

This book introduces factor analysis methods for the study of multivariate stochastic processes whose structure evolves over time. It introduces the theory of factor analysis and evolutionary stochastic processes and combines that to study evolutionary factor models. The book includes many real examples to illustrate applications from such fields as macroeconomics, finance, neuroscience, communication, and psychology. MATLAB® and R code is included throughout the book for implementation of the methods.

This book introduces factor analysis methods for the study of multivariate stochastic processes whose structure evolves over time. It introduces the theory of factor analysis and evolutionary stochastic processes and combines that to study evolutionary factor models. The book includes many real examples to illustrate applications from such fields as macroeconomics, finance, neuroscience, communication, and psychology. MATLAB® and R code is included throughout the book for implementation of the methods.

Evolutionary Factor Analysis

Asymptotics for Fractional Processes develops an approach to the large-sample analysis of fractional partial-sum processes, featuring long memory increments.

Asymptotics for Fractional Processes

The book offers insight into the healthcare system by exploring emerging technologies and AI-based applications and implementation strategies. It includes current developments for future directions as well as covering the concept of the healthcare system along with its ecosystem.

Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem

Chapman & Hall/CRC The R Series

Copula additive distributional regression enables the joint modeling of multiple outcomes, an essential aspect of many real-world research problems. This book provides an accessible overview of this modeling approach, with a particular focus on its implementation in the GJRM R package

Copula Additive Distributional Regression Using R

An essential guide to the ways data can improve decision making. Statistics are everywhere: in news reports, at the doctor's office, and in every sort of forecast, from the stock market to the weather. Blogger, teacher, and computer scientist Allen B. Downey knows well that people have an innate ability both to understand statistics and to be fooled by them. As he makes clear in this accessible introduction to statistical thinking, the stakes are big. Simple misunderstandings have led to incorrect medical prognoses, underestimated the likelihood of large earthquakes, hindered social justice efforts, and resulted in dubious policy decisions. There are right and wrong ways to look at numbers, and Downey will help you see which are which. Probably Overthinking It uses real data to delve into real examples with real consequences, drawing on cases from health campaigns, political movements, chess rankings, and more. He lays out common pitfalls--like the base rate fallacy, length-biased sampling, and Simpson's paradox--and shines a light on what we learn when we interpret data correctly, and what goes wrong when we don't. Using data visualizations instead of equations, he builds understanding from the basics to help you recognize errors, whether in your own thinking or in media reports. Even if you have never studied statistics--or if you have and forgot everything you learned--this book will offer new insight into the methods and measurements that help us understand the world.

How to Use Data to Answer Questions, Avoid Statistical Traps, and Make Better Decisions

Probably Overthinking It

The Converse of Bayes Theorem with Applications

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