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Essential Bayesian Models de C. R. Rao

Essential Bayesian Models

Autor C. R. Rao, Dipak K. Dey
en Limba Engleză Hardback – 16 noi 2010
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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Specificații

ISBN-13: 9780444537324
ISBN-10: 0444537325
Pagini: 586
Ilustrații: 1
Dimensiuni: 152 x 229 x 33 mm
Greutate: 1.04 kg
Editura: ELSEVIER SCIENCE

Public țintă

Scientists, Engineers, Professionals, and Researchers in Biomedical Engineering, Molecular Biologists, Computer Engineers, Software Engineers, Biological Scientists, Computer hardware engineers, Biomedical engineers, Mechanical engineers, Systems Engineers, and Software Engineers

Cuprins

1. Model Selection and Hypothesis Testing based on Objective Probabilities and Bayes Factors; 2. Bayesian Model Checking and Model Diagnostics; 3. Bayesian Nonparametric Modeling and Data Analysis: An Introduction; 4. Some Bayesian Nonparametric Models; 5. Bayesian Modeling in the Wavelet Domain; 6. Bayesian Methods for Function Estimation; 7. MCMC Methods to Estimate Bayesian Parametric Models; 8. Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities; 9. Bayesian Modelling and Inference on Mixtures of Distributions; 10. Variable Selection and Covariance Selection in Multivariate Regression Models; 11. Dynamic Models; 12. Elliptical Measurement Error Models – A Bayesian Approach; 13. Bayesian Sensitivity Analysis in Skew-elliptical Models; 14. Bayesian Methods for DNA Microarray Data Analysis; 15. Bayesian Biostatistics; 16. Innovative Bayesian Methods for Biostatistics and Epidemiology; 17. Modeling and Analysis for Categorical Response Data; 18. Bayesian Methods and Simulation-Based Computation for Contingency Tables; 19. Teaching Bayesian Thought to Nonstatisticians