Bayesian Thinking, Modeling and Computation

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  • Bayesian Thinking, Modeling and Computation Book Detail

  • Author :
  • Release Date : 2005-11-29
  • Publisher : Elsevier
  • Genre : Mathematics
  • Pages : 1062
  • ISBN 13 : 0080461174
  • File Size : 6,6 MB
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Bayesian Thinking, Modeling and Computation by PDF Summary

Book Description: 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

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Bayesian Thinking, Modeling and Computation

Bayesian Thinking, Modeling and Computation

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This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It furt

Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python

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Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3

Essential Bayesian Models

Essential Bayesian Models

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This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook of Statistics Series, wit

Bayesian Computation with R

Bayesian Computation with R

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There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity