Bayesian inference

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.

Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics.

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics.

The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests.

This book collects select chapters on modern industrial problems related to uncertainties and vagueness in the expert domain of knowledge.

This book focuses on the structural analysis of demand under block rate pricing, a type of nonlinear pricing used mainly in public utility services.

This book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019.

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

This book walks you through learning probability and statistics from a Bayesian point of view.

This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data.

This volume focuses on predicting users' attendance at a future event at specific time and location based on their common interests.

This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology.

Bayesian Networks This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.

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

This book discusses all major topics on survey sampling and estimation.

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization.

This book provides a comprehensive introduction to statistical approaches for the assessment of complex environmental exposures, such as pollutants and chemical mixtures, within the exposome framework.

This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious.

This book explains the importance of using the probability that the hypothesis is correct (PHC), an intuitive measure that anyone can understand, as an alternative to the p-value.

This book walks you through learning probability and statistics from a Bayesian point of view.

If you know how to program, you're ready to tackle Bayesian statistics.

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

This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R.

This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area.

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering.

The book shows how risk, defined as the statistical expectation of loss, can be formally decomposed as the product of two terms: hazard probability and system vulnerability.

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.

This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology.

This book represents a compilation of experience from a slate of experts involved in developing and deploying Bayesian Networks (BN) for corrosion management.

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization.

This book collects select chapters on modern industrial problems related to uncertainties and vagueness in the expert domain of knowledge.

This volume on the latest developments in statistical modelling is a collection of refereed papers presented at the 38th International Workshop on Statistical Modelling, IWSM 2024, held from 14 to 19 July 2024 in Durham, UK.

This book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics.

This book introduces the major concepts of probability and statistics, along with the necessary computational tools, for undergraduates and graduate students.

Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor.

This book provides, as simply as possible, sound foundations for an in-depth understanding of reliability engineering with regard to qualitative analysis, modelling, and probabilistic calculations of safety and production systems.

This volume focuses on predicting users' attendance at a future event at specific time and location based on their common interests.

This edited collection brings together internationally recognized experts in a range of areas of statistical science to honor the contributions of the distinguished statistician, Barry C.

This book provides a self-contained introduction of mixed-effects models and small area estimation techniques.

The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development.

Bayesian analysis is one of the important tools for statistical modelling and inference.

This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors.

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

This book introduces some recent developments in Bayesian real-time system identification.

This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking.

The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development.

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

This book presents recent advances of Bayesian inference in structured tensor decompositions.

This book discusses all major topics on survey sampling and estimation.