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Bayesian Thinking, Modeling and Computation

2005-11-29 Mathematics
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Release : 2005-11-29
Genre : Mathematics
Kind : eBook
Book Rating : 174/5 ( reviews)

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Book Synopsis Bayesian Thinking, Modeling and Computation by :

Download or read book Bayesian Thinking, Modeling and Computation written by . This book was released on 2005-11-29. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics

Bayesian Thinking

2005 Bayesian statistical decision theory
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Release : 2005
Genre : Bayesian statistical decision theory
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Bayesian Thinking by : Dipak Dey

Download or read book Bayesian Thinking written by Dipak Dey. This book was released on 2005. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Modeling and Computation in Python

2021-12-28 Computers
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Release : 2021-12-28
Genre : Computers
Kind : eBook
Book Rating : 048/5 ( reviews)

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Book Synopsis Bayesian Modeling and Computation in Python by : Osvaldo A. Martin

Download or read book Bayesian Modeling and Computation in Python written by Osvaldo A. Martin. This book was released on 2021-12-28. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.

Essential Bayesian Models

2010-12-30 Mathematics
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Release : 2010-12-30
Genre : Mathematics
Kind : eBook
Book Rating : 324/5 ( reviews)

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Book Synopsis Essential Bayesian Models by : C.R. Rao

Download or read book Essential Bayesian Models written by C.R. Rao. This book was released on 2010-12-30. Available in PDF, EPUB and Kindle. Book excerpt: This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric Bayesian methods for modeling, and how to use modern computational methods to summarize inferences using simulation. The book covers a wide range of topics including objective and subjective Bayesian inferences, with a variety of applications in modeling categorical, survival, spatial, spatiotemporal, Epidemiological, small area and micro array data.

Bayesian Computation with R

2009-04-20 Mathematics
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Release : 2009-04-20
Genre : Mathematics
Kind : eBook
Book Rating : 989/5 ( reviews)

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Book Synopsis Bayesian Computation with R by : Jim Albert

Download or read book Bayesian Computation with R written by Jim Albert. This book was released on 2009-04-20. Available in PDF, EPUB and Kindle. Book excerpt: There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

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