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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/69730 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/69730 http://eprints.org/relation/ismediumThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/69730 69772 2 2410033 document 69772 small.jpg image/png 847db1003f1aef8686830fd2ccc9e7c2 MD5 1750 2020-10-16 01:38:52 https://vuir.vu.edu.au/1725/5/small.jpg 1725 5 5 image/png other Thumbnails conversion from text to thumbnail_small en staffonly

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/69730 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/69730 http://eprints.org/relation/issmallThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/69730 archive 293 disk0/00/00/17/25 2009-02-04 13:24:15 2021-08-27 07:42:03 2009-02-04 13:24:15 article show Huang Fuchun Huang, Fuchun Ogata Yosihiko Ogata, Yosihiko pub 290000 tafeengineering gibbs sampling, likelihood, MCMC integration, metropolis algorithm, partition function Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model. 2001-03 published Wiley-Blackwell Publishing Asia 10.1111/1467-842X.00154 http://dx.doi.org/10.1111/1467-842X.00154 69730 2 2409978 dark_document 69730 Huang Fuchun.pdf application/pdf e291d1aa0a868cff2cbce8e484ef15e5 MD5 72899 2020-10-16 01:28:39 1725 1 1 application/pdf text Generic Permission Form en staffonly

Huang Fuchun.pdf

other none Australian & New Zealand Journal of Statistics 43 1 47-65 TRUE 1369-1473 FALSE 10 2-s2.0-0035579701 2023-07-02 04:01:01