Bayesian inference

Introduction to Probability Models, Ninth Edition, is the primary text for a first undergraduate course in applied probability.

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 diversity of interesting applications.

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

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

The Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability represents a monumental effort in advancing the discourse on probability theory and statistical applications across a wide range of scientific disciplines.

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

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

Harmonic Analysis and the Theory of Probability by Salomon Bochner is a landmark monograph that rigorously links two central domains of modern mathematics: Fourier analysis and probability theory.

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.

This book describes how Bayesian methods work.

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

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

The Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability continues the tradition of this esteemed series, which has been held every five years since 1945.

A hands-on guide to Bayesian models with R, JAGS, Python, and Stan code, for a wide range of astronomical data types.

The International Bestseller by 'The Galileo of number crunchers' (Independent)Every time we choose a route to work, decide whether to go on a second date, or set aside money for a rainy day, we are making a prediction about the future.

This Element highlights the archaeology of methods developed in chemometrics, artificial intelligence, and Bayesian statistics.

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.

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 describes how Bayesian methods work.

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 diversity of interesting applications.

Rudolf Carnap and Richard C.

This book describes how Bayesian methods work.

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.

Rudolf Carnap and Richard C.

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

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

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.

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

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 introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.

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.

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

This book presents a comprehensive introduction to well logging and the inverse problem.

A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis.

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

The Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability continues the tradition of this esteemed series, which has been held every five years since 1945.

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

Harmonic Analysis and the Theory of Probability by Salomon Bochner is a landmark monograph that rigorously links two central domains of modern mathematics: Fourier analysis and probability theory.

Researchers across the natural and social sciences find themselves navigating tremendous amounts of new data.

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

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.