Concave-Convex Adaptive Rejection Sampling

Journal article

We describe a method for generating independent samples from univariate density functions using adaptive rejection sampling without the log-concavity requirement. The method makes use of the fact that many functions can be expressed as a sum of concave and convex functions. Using a concave-convex decomposition, we bound the logdensity by separately bounding the concave and convex parts using piecewise linear functions. The upper bound can then be used as the proposal distribution in rejection...

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Authors: Goeruer, D; Teh, Y

• Publication status: Published
• Journal: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
• Volume: 20
• Issue: 3
• Pages: 670-691
• Publication date: 2011-09-01
• DOI: 10.1198/jcgs.2011.09058
• EISSN: 1537-2715
• ISSN: 1061-8600
• Language: English
• Keywords: Monte Carlo sampling; Log-concave densities; Decomposition; Random number generation