Bayesian Inference for Stochastic Processes

This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.

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  • Author : Lyle D. Broemeling
  • Publisher : CRC Press
  • Pages : 432 pages
  • ISBN : 1315303582
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKBayesian Inference for Stochastic Processes

Bayesian Inference for Stochastic Processes

Bayesian Inference for Stochastic Processes
  • Author : Lyle D. Broemeling
  • Publisher : CRC Press
  • Release : 12 December 2017
GET THIS BOOKBayesian Inference for Stochastic Processes

This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are

Bayesian Inference

Bayesian Inference
  • Author : William A Link,Richard J Barker
  • Publisher : Academic Press
  • Release : 07 August 2009
GET THIS BOOKBayesian Inference

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many

Bayesian Inference

Bayesian Inference
  • Author : Hanns L. Harney
  • Publisher : Springer Science & Business Media
  • Release : 14 March 2013
GET THIS BOOKBayesian Inference

Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. The text is written at introductory level, with many examples and exercises.

Bayesian Inference

Bayesian Inference
  • Author : Hanns Ludwig Harney
  • Publisher : Springer
  • Release : 18 October 2016
GET THIS BOOKBayesian Inference

This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach

Bayesian Inference in Statistical Analysis

Bayesian Inference in Statistical Analysis
  • Author : George E. P. Box,George C. Tiao
  • Publisher : John Wiley & Sons
  • Release : 25 January 2011
GET THIS BOOKBayesian Inference in Statistical Analysis

Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison

Likelihood and Bayesian Inference

Likelihood and Bayesian Inference
  • Author : Leonhard Held,Daniel Sabanés Bové
  • Publisher : Springer Nature
  • Release : 31 March 2020
GET THIS BOOKLikelihood and Bayesian Inference

This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference. Topics include

Perception as Bayesian Inference

Perception as Bayesian Inference
  • Author : David C. Knill,Whitman Richards
  • Publisher : Cambridge University Press
  • Release : 13 September 1996
GET THIS BOOKPerception as Bayesian Inference

Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This 1996 book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modelling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between different perspectives both within

Evaluating Great Lakes Bald Eagle Nesting Habitat with Bayesian Inference

Evaluating Great Lakes Bald Eagle Nesting Habitat with Bayesian Inference
  • Author : Teryl G. Grubb
  • Publisher : Unknown Publisher
  • Release : 09 May 2021
GET THIS BOOKEvaluating Great Lakes Bald Eagle Nesting Habitat with Bayesian Inference

Bayesian inference facilitated structured interpretation of a nonreplicated, experience-based survey of potential nesting habitat for bald eagles (Haliaeetus leucocephalus) along the five Great Lakes shorelines. We developed a pattern recognition (PATREC) model of our aerial search image with six habitat attributes: (a) tree cover, (b) proximity and (c) type/amount of human disturbance, (d) potential foraging habitat/shoreline irregularity, and suitable trees for (e) perching and (f) nesting. Tree cover greater than 10 percent, human disturbance more than 0.8 km away, a

Bayesian Inference for Partially Identified Models

Bayesian Inference for Partially Identified Models
  • Author : Paul Gustafson
  • Publisher : CRC Press
  • Release : 01 April 2015
GET THIS BOOKBayesian Inference for Partially Identified Models

Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs. The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties

Bayesian Inference

Bayesian Inference
  • Author : Javier Prieto Tejedor
  • Publisher : BoD – Books on Demand
  • Release : 02 November 2017
GET THIS BOOKBayesian Inference

The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It

Bayesian Methods for Hackers

Bayesian Methods for Hackers
  • Author : Cameron Davidson-Pilon
  • Publisher : Addison-Wesley Professional
  • Release : 30 September 2015
GET THIS BOOKBayesian Methods for Hackers

Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming

Statistical Inference

Statistical Inference
  • Author : Murray Aitkin
  • Publisher : CRC Press
  • Release : 02 June 2010
GET THIS BOOKStatistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing. After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout.