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Bayesian Inference in Econometric Models Using Monte Carlo Integration
Abstract
Methods for the systematic application of Monte Carlo integration with importance sampling to Bayesian inference are developed. Conditions under which the numerical approximation converges almost surely to the true value with the number of Monte Carlo replications, and its numerical accuracy may be assessed reliably, are given. Importance sampling densities are derived from multivariate normal or student approximations to the posterior density. These densities are modified by automatic rescaling along each axis. The concept of relative numerical efficiency is introduced to evaluate the adequacy of a chosen importance sampling density. Applications in two illustrative models are presented. Copyright 1989 by The Econometric Society.
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Cited by
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
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Train, K. E. (2001) · doi:10.1017/cbo9780511805271 · Cited by 7,784
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Assessment and Propagation of Model Uncertainty
Draper, D. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02015.x · Cited by 1,599
Localization for mobile sensor networks
Hu, L., & Evans, D. (2004) · Proceedings of the 10th annual international conference on Mobile computing and networking · doi:10.1145/1023720.1023726 · Cited by 1,050
Metadata
- Authors
- Geweke, J.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1989-11
- Volume
- 57
- Issue
- 6
- Pages
- 1317
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2057565703
- Cited by
- 1,563
- References
- 0
- FWCI
- 22.97
- Type
- journal article
- Language
- en
- Open access
- closed
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