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Monte Carlo Estimation of Bayesian Credible and HPD Intervals
Abstract
This article considers how to estimate Bayesian credible and highest probability density (HPD) intervals for parameters of interest and provides a simple Monte Carlo approach to approximate these Bayesian intervals when a sample of the relevant parameters can be generated from their respective marginal posterior distribution using a Markov chain Monte Carlo (MCMC) sampling algorithm. We also develop a Monte Carlo method to compute HPD intervals for the parameters of interest from the desired posterior distribution using a sample from an importance sampling distribution. We apply our methodology to a Bayesian hierarchical model that has a posterior density containing analytically intractable integrals that depend on the (hyper) parameters. We further show that our methods are useful not only for calculating the HPD intervals for the parameters of interest but also for computing the HPD intervals for functions of the parameters. Necessary theory is developed and illustrative examples—including a simulation study—are given.
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Cites
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
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Metadata
- Authors
- Chen, M.-H., & Shao, Q.-M.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 1999-03
- Volume
- 8
- Issue
- 1
- Pages
- 69-92
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W2084568079
- Cited by
- 1,209
- References
- 1
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, M.-H., & Shao, Q.-M. (1999). Monte Carlo Estimation of Bayesian Credible and HPD Intervals. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.1999.10474802
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
