Bayesian 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.

Produk Detail:

  • Author : Hanns L. Harney
  • Publisher : Springer Science & Business Media
  • Pages : 263 pages
  • ISBN : 9783540003977
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKBayesian Inference

Bayesian Inference

Bayesian Inference
  • Author : Hanns L. Harney
  • Publisher : Springer Science & Business Media
  • Release : 20 May 2003
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 with Geodetic Applications

Bayesian Inference with Geodetic Applications
  • Author : Karl-Rudolf Koch
  • Publisher : Springer
  • Release : 11 April 2006
GET THIS BOOKBayesian Inference with Geodetic Applications

This introduction to Bayesian inference places special emphasis on applications. All basic concepts are presented: Bayes' theorem, prior density functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, mixed model and in the model with unknown variance and covariance components is considered. Solutions are supplied for the classification, for the

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

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

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

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

Bayesian Inference in Wavelet-Based Models

Bayesian Inference in Wavelet-Based Models
  • Author : Peter Müller,Brani Vidakovic
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKBayesian Inference in Wavelet-Based Models

This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional

Bayesian Inference in Dynamic Econometric Models

Bayesian Inference in Dynamic Econometric Models
  • Author : Luc Bauwens,Michel Lubrano,Jean-François Richard
  • Publisher : OUP Oxford
  • Release : 06 January 2000
GET THIS BOOKBayesian Inference in Dynamic Econometric Models

This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series,

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 : 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

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

Bayesian Inference on Complicated Data

Bayesian Inference on Complicated Data
  • Author : Niansheng Tang
  • Publisher : BoD – Books on Demand
  • Release : 15 July 2020
GET THIS BOOKBayesian Inference on Complicated Data

Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer

An Introduction to Bayesian Inference in Econometrics

An Introduction to Bayesian Inference in Econometrics
  • Author : Arnold Zellner
  • Publisher : New York : J. Wiley
  • Release : 26 November 1971
GET THIS BOOKAn Introduction to Bayesian Inference in Econometrics

Remarks on inference in economics; Principles of bayesian analysis with selected applications; The univariate normal linear regression model; Special problems in regression analysis; On error in the variables; Analysis of single equation nonlinear models; Time series models: some selected examples; Multivariate regression models; Simultaneous equation econometric models; On comparing and testing hypotheses; Analysis of some control problems.