Essential Bayesian Models

By:

Rao, C.R [author.]

Contributor(s):

Dey, Dipak K [author.]

Material type:

TextLanguage: English Publication details: Amsterdam : Elsevier, 2011.Description: xii, 574pISBN:

9780444537324

Subject(s):

Other classification:

B288 Q1 TB

Summary: 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. Aids critical thinking on causal effects Provides simulation based computing techniques Covers Bioinformatics and Biostatistics.

Item type:

Textbook

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Textbook	Central Science Library	Central Science Library			B288 Q1 TB (Browse shelf(Opens below))					Available			SL1558054

Includes Bibliographical References and Subject Index 559-574p.

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.
Aids critical thinking on causal effects Provides simulation based computing techniques Covers Bioinformatics and Biostatistics.

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