Bayesian Inference

Bayesian Inference
Author: Hanns L. Harney
Publsiher: Springer Science & Business Media
Total Pages: 263
Release: 2013-03-14
ISBN 10: 366206006X
ISBN 13: 9783662060063
Language: EN, FR, DE, ES & NL

Bayesian Inference Book Review:

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 in Statistical Analysis

Bayesian Inference in Statistical Analysis
Author: George E. P. Box,George C. Tiao
Publsiher: John Wiley & Sons
Total Pages: 608
Release: 2011-01-25
ISBN 10: 111803144X
ISBN 13: 9781118031445
Language: EN, FR, DE, ES & NL

Bayesian Inference in Statistical Analysis Book Review:

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 of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

Bayesian Inference

Bayesian Inference
Author: William A Link,Richard J Barker
Publsiher: Academic Press
Total Pages: 354
Release: 2009-08-07
ISBN 10: 0080889808
ISBN 13: 9780080889801
Language: EN, FR, DE, ES & NL

Bayesian Inference Book Review:

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 relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analytical software and examples Leading authors with world-class reputations in ecology and biostatistics

Perception as Bayesian Inference

Perception as Bayesian Inference
Author: David C. Knill,Whitman Richards
Publsiher: Cambridge University Press
Total Pages: 516
Release: 1996-09-13
ISBN 10: 9780521461092
ISBN 13: 052146109X
Language: EN, FR, DE, ES & NL

Perception as Bayesian Inference Book Review:

This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.

Modelling Operational Risk Using Bayesian Inference

Modelling Operational Risk Using Bayesian Inference
Author: Pavel V. Shevchenko
Publsiher: Springer Science & Business Media
Total Pages: 302
Release: 2011-01-19
ISBN 10: 9783642159237
ISBN 13: 3642159230
Language: EN, FR, DE, ES & NL

Modelling Operational Risk Using Bayesian Inference Book Review:

The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate. This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks. This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk.

Bayesian Inference in Dynamic Econometric Models

Bayesian Inference in Dynamic Econometric Models
Author: Luc Bauwens,Michel Lubrano,Jean-François Richard
Publsiher: OUP Oxford
Total Pages: 366
Release: 2000-01-06
ISBN 10: 0191588466
ISBN 13: 9780191588464
Language: EN, FR, DE, ES & NL

Bayesian Inference in Dynamic Econometric Models Book Review:

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, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

Bayesian Inference for Gene Expression and Proteomics

Bayesian Inference for Gene Expression and Proteomics
Author: Kim-Anh Do,Peter Müller,Marina Vannucci
Publsiher: Cambridge University Press
Total Pages: 437
Release: 2006-07-24
ISBN 10: 052186092X
ISBN 13: 9780521860925
Language: EN, FR, DE, ES & NL

Bayesian Inference for Gene Expression and Proteomics Book Review:

Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.

Bayesian Inference

Bayesian Inference
Author: Javier Prieto Tejedor
Publsiher: BoD – Books on Demand
Total Pages: 378
Release: 2017-11-02
ISBN 10: 9535135775
ISBN 13: 9789535135777
Language: EN, FR, DE, ES & NL

Bayesian Inference Book Review:

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 is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.

Bayesian Inference

Bayesian Inference
Author: Hanns Ludwig Harney
Publsiher: Springer
Total Pages: 243
Release: 2016-10-18
ISBN 10: 3319416448
ISBN 13: 9783319416441
Language: EN, FR, DE, ES & NL

Bayesian Inference Book Review:

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 provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.

Bayesian Inference for Probabilistic Risk Assessment

Bayesian Inference for Probabilistic Risk Assessment
Author: Dana Kelly,Curtis Smith
Publsiher: Springer Science & Business Media
Total Pages: 228
Release: 2011-08-30
ISBN 10: 9781849961875
ISBN 13: 1849961875
Language: EN, FR, DE, ES & NL

Bayesian Inference for Probabilistic Risk Assessment Book Review:

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.

Bayesian Inference with Geodetic Applications

Bayesian Inference with Geodetic Applications
Author: Karl-Rudolf Koch
Publsiher: Springer
Total Pages: 199
Release: 2006-04-11
ISBN 10: 3540466010
ISBN 13: 9783540466017
Language: EN, FR, DE, ES & NL

Bayesian Inference with Geodetic Applications Book Review:

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 posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.

Bayesian Inference in the Social Sciences

Bayesian Inference in the Social Sciences
Author: Ivan Jeliazkov,Xin-She Yang
Publsiher: John Wiley & Sons
Total Pages: 352
Release: 2014-11-04
ISBN 10: 1118771125
ISBN 13: 9781118771129
Language: EN, FR, DE, ES & NL

Bayesian Inference in the Social Sciences Book Review:

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

Bayesian Inference of Nanoparticle Broadened

Bayesian Inference of Nanoparticle Broadened
Author: Anonim
Publsiher: DIANE Publishing
Total Pages: 329
Release:
ISBN 10: 9781422329757
ISBN 13: 1422329755
Language: EN, FR, DE, ES & NL

Bayesian Inference of Nanoparticle Broadened Book Review:

Learning Statistics

Learning Statistics
Author: Anonim
Publsiher: Anonim
Total Pages: 329
Release:
ISBN 10:
ISBN 13: OCLC:1181886566
Language: EN, FR, DE, ES & NL

Learning Statistics Book Review:

Turn to an entirely different approach for doing statistical inference: Bayesian statistics, which assumes a known prior probability and updates the probability based on the accumulation of additional data. Unlike the frequentist approach, the Bayesian method does not depend on an infinite number of hypothetical repetitions. Explore the flexibility of Bayesian analysis.

Bayesian Inference for Stable Differential Equation Models with Applications in Computational Neuroscience

Bayesian Inference for Stable Differential Equation Models with Applications in Computational Neuroscience
Author: Philip John Maybank
Publsiher: Anonim
Total Pages: 329
Release: 2019
ISBN 10:
ISBN 13: OCLC:1114806865
Language: EN, FR, DE, ES & NL

Bayesian Inference for Stable Differential Equation Models with Applications in Computational Neuroscience Book Review:

Why Bayesian Inference

Why Bayesian Inference
Author: George Arhonditsis
Publsiher: Anonim
Total Pages: 329
Release: 2018
ISBN 10:
ISBN 13: OCLC:1189441929
Language: EN, FR, DE, ES & NL

Why Bayesian Inference Book Review:

Bayesian Data Analysis Second Edition

Bayesian Data Analysis  Second Edition
Author: Andrew Gelman,John B. Carlin,Hal S. Stern,Donald B. Rubin
Publsiher: CRC Press
Total Pages: 696
Release: 2003-07-29
ISBN 10: 1420057294
ISBN 13: 9781420057294
Language: EN, FR, DE, ES & NL

Bayesian Data Analysis Second Edition Book Review:

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

Comparative Statistical Inference

Comparative Statistical Inference
Author: Vic Barnett
Publsiher: John Wiley & Sons
Total Pages: 410
Release: 2009-09-25
ISBN 10: 0470317795
ISBN 13: 9780470317792
Language: EN, FR, DE, ES & NL

Comparative Statistical Inference Book Review:

This fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision-making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what assumptions they are based on, and the practical implications of such distinctions. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts. Includes fully updated and revised material from the successful second edition Recent changes in emphasis, principle and methodology are carefully explained and evaluated Discusses all recent major developments Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood etc) Includes extensive references and bibliography Written by a well-known and respected author, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.

Bayesian inference with INLA

Bayesian inference with INLA
Author: Virgilio Gomez-Rubio
Publsiher: CRC Press
Total Pages: 316
Release: 2020-02-20
ISBN 10: 1351707205
ISBN 13: 9781351707206
Language: EN, FR, DE, ES & NL

Bayesian inference with INLA Book Review:

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

An Introduction to Bayesian Analysis

An Introduction to Bayesian Analysis
Author: Jayanta K. Ghosh,Mohan Delampady,Tapas Samanta
Publsiher: Springer Science & Business Media
Total Pages: 354
Release: 2007-07-03
ISBN 10: 0387354336
ISBN 13: 9780387354330
Language: EN, FR, DE, ES & NL

An Introduction to Bayesian Analysis Book Review:

This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior.