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Monte Carlo Methods in Bayesian Computation
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Introduction
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Chapter 2
Markov Chain Monte Carlo Sampling
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Chapter 3
Basic Monte Carlo Methods for Estimating Posterior Quantities
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Chapter 4
Estimating Marginal Posterior Densities
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Chapter 5
Estimating Ratios of Normalizing Constants
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Chapter 6
Monte Carlo Methods for Constrained Parameter Problems
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Chapter 7
Computing Bayesian Credible and HPD Intervals
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Chapter 8
Bayesian Approaches for Comparing Nonnested Models
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Chapter 9
Bayesian Variable Selection
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Chapter 10
Other Topics
Overall attention for this book and its chapters
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Mentioned by
blogs
1
blog
twitter
1
X user
syllabi
2
institutions with syllabi
wikipedia
1
Wikipedia page
Readers on
mendeley
16
Mendeley
Book overview
1. Introduction
2. Markov Chain Monte Carlo Sampling
3. Basic Monte Carlo Methods for Estimating Posterior Quantities
4. Estimating Marginal Posterior Densities
5. Estimating Ratios of Normalizing Constants
6. Monte Carlo Methods for Constrained Parameter Problems
7. Computing Bayesian Credible and HPD Intervals
8. Bayesian Approaches for Comparing Nonnested Models
9. Bayesian Variable Selection
10. Other Topics
Summary
Blogs
X
Syllabi
Wikipedia
This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
3
syllabi from
2
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
University of California-Los Angeles
2
Unknown
University of North Carolina at Chapel Hill
1
Unknown