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Introducing Markov chain Monte Carlo
Abstract
Most applications of MCMC to date, including the majority of those described in the following chapters, are oriented towards Bayesian inference. From a Bayesian perspective, there is no fundamental distinction between The integrations in this expression have until recently been the source of most of the practical difficulties in Bayesian inference, especially in high dimensions. In most applications, analytic evaluation of E[J(0)ID] is impossible. Alternative approaches include numerical evaluation, which is difficult and inaccurate in greater than about 20 dimensions; analytic approximation such as the Laplace approximation (Kass et al., 1988), which is sometimes appropriate; and Monte Carlo integration, including MCMC.
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Cited by
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
Generalized linear mixed models: a practical guide for ecology and evolution
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2008.10.008 · Cited by 8,910
Landscape effects on crop pollination services: are there general patterns?
Ricketts, T. H., Regetz, J., Steffan‐Dewenter, I., Cunningham, S. A., Kremen, C., Bogdanski, A., Gemmill‐Herren, B., Greenleaf, S. S., Klein, A. M., Mayfield, M. M., et al. (2008) · Ecology Letters · doi:10.1111/j.1461-0248.2008.01157.x · Cited by 1,379
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Metadata
- Venue
- Markov Chain Monte Carlo in Practice
- Publisher
- Chapman and Hall/CRC
- Published
- 1995-12-01
- Pages
- 19-38
- OpenAlex
- W74953171
- Cited by
- 510
- References
- 0
- FWCI
- 38.04
- Type
- book chapter
- Language
- en
- Open access
- closed
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