Bayesian inference

The idea of the book is to present a text that is useful for both students of quantitative sciences and practitioners who work with univariate or multivariate probabilistic models.

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

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

The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics.

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

This textbook reconstructs the statistics curriculum from the perspective of posterior probability.

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

This book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution.

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.

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods.

This book provides an extensive coverage of the methodology of survival analysis, ranging from introductory level material to deeper more advanced topics.

Highlights the advantages of using the tripartite model within a Bayesian framework for estimation of time calibrated phylogenetic trees.

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.

This book provides an account of weak convergence theory, empirical processes, and their application to a wide variety of problems in statistics.

This book explores how citizenship is differently gendered and performed across national and regional boundaries.

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 book is a compilation of unpublished papers written by Jean-Marie Rolin (with several co-authors) on nonparametric bayesian estimation.

This book describes how Bayesian methods work.

A comprehensive resource that offers an introduction to statistics with a Bayesian angle, for students of professional disciplines like engineering and economics The Bayesian Way offers a basic introduction to statistics that emphasizes the Bayesian approach and is designed for use by those studying professional disciplines like engineering and economics.

This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data.

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.

This book is a compilation of unpublished papers written by Jean-Marie Rolin (with several co-authors) on nonparametric bayesian estimation.

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

This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data.

This book describes how Bayesian methods work.

Bayesian statistics is currently undergoing something of a renaissance.

This book describes how Bayesian methods work.

This comprehensive book covers a wide variety of methods for estimating the sizes and related parameters of closed populations.

Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective for graduate students and researchers.

This monograph focuses on those stochastic quickest detection tasks in disorder problems that arise in the dynamical analysis of statistical data.

This book first provides a review of various aspects of Bayesian statistics.

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.

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

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.

Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems.

Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems.

A comprehensive resource that offers an introduction to statistics with a Bayesian angle, for students of professional disciplines like engineering and economics The Bayesian Way offers a basic introduction to statistics that emphasizes the Bayesian approach and is designed for use by those studying professional disciplines like engineering and economics.

This monograph focuses on those stochastic quickest detection tasks in disorder problems that arise in the dynamical analysis of statistical data.

This textbook reconstructs the statistics curriculum from the perspective of posterior probability.

This book first provides a review of various aspects of Bayesian statistics.

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets.

This book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution.

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

This book provides a comprehensive treatment of the logic behind hypothesis testing.

Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot.