Sampling Some Truncated Distributions Via Rejection Algorithms

In this article, we develop rejection sampling algorithms to sample from some truncated and tail distributions. Such samplers are needed in many Markov chain Monte Carlo methods, often in connection with Bayesian inference. In addition to univariate normal, gamma, and beta distributions, we consider...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2010-06, Vol.39 (6), p.1111-1121
Main Authors: Laud, Purushottam W., Damien, Paul, Shively, Thomas S.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Computation
Computer simulation
Distribution theory
Exact sciences and technology
Gibbs sampler
Markov analysis
Markov chain Monte Carlo
Markov processes
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Parametric inference
Probability and statistics
Probability theory and stochastic processes
Rejection
Sampling
Sciences and techniques of general use
Statistics
Tail probabilities
Truncated densities
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Summary:In this article, we develop rejection sampling algorithms to sample from some truncated and tail distributions. Such samplers are needed in many Markov chain Monte Carlo methods, often in connection with Bayesian inference. In addition to univariate normal, gamma, and beta distributions, we consider multivariate normal distributions truncated to certain sets.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2010.484117