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Markov Chain Monte Carlo stochastic differential equations

2013-11-24 03:16:10
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Markov Monte Carlo (MCMC) methods (including random walk Monte Carlo methods) is a set of algorithms by Markov chains from randomly sampled DIBEN steps before. More results, the better.

Establishment of a Markov chain is not difficult for a specified property. Difficult is how to reach qualified figures within permission errors. Markov chains can also be fast mixing phase – starting from rapid access to a stable status-please refer to maximum time for Markov chains .

Due to the initial sample, the most common of MCMC sampling can only approximate to be distributed. Complex of improved MCMC algorithms such as coupling in the past , but they consume more computing resources and time.

Typical use is to simulate a pedestrian path optimization of a random walk. Every one of them count as a State. Without statistics through most places will more likely destined for the next step. Markov Monte Carlo method is a combination of the Monte Carlo method to solution. But unlike previous Monte Carlo integration are statistically independent , MCMC is statistically relevant .

Related applications of this method include the following: Bayesian statistics , and Computational Physics , and computational biology and computational linguistics , in addition to Mr Gill's writings.

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<>0.00 B2009-10-08 10:33
DESCRIPTION380.00 B2009-10-08 16:06
<>0.00 B2009-08-28 12:24
<>0.00 B2009-10-08 16:06
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metrop.pdf90.10 kB2009-08-28 15:22
demo.pdf1,011.60 kB2009-10-08 16:06
debug.Rnw12.62 kB2009-10-08 10:22
temper.tex10.61 kB2009-08-28 15:06
demo.Rnw20.24 kB2009-09-20 17:47
Makefile306.00 B2009-10-08 10:29
temper.pdf95.75 kB2009-08-28 15:22
demo.tex24.66 kB2009-10-08 16:06
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metrop.tex15.14 kB2009-08-28 12:24
<>0.00 B2009-10-08 10:23
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initseq.R1.16 kB2009-08-28 12:24
logit.Rout.save4.77 kB2009-08-28 12:24
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<>0.00 B2009-10-08 14:53
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<>0.00 B2009-09-08 06:23
foo.txt5.55 kB2009-09-07 16:14
logit.txt2.72 kB2009-09-08 06:23
NAMESPACE194.00 B2009-08-29 13:32
COPYING1.49 kB2009-08-28 12:24
<>0.00 B2009-10-08 16:06
getListElement.c1.97 kB2009-08-28 12:24
metrop.c16.81 kB2009-10-07 19:09
getScalarInteger.c1.96 kB2009-08-29 14:49
myutil.h1.74 kB2009-08-29 14:51
isAllFinite.c1.83 kB2009-08-28 12:24
olbm.c3.18 kB2009-08-28 12:24
temper.c41.80 kB2009-10-08 09:24
getScalarLogical.c1.84 kB2009-08-29 14:52
initseq.c3.59 kB2009-08-28 12:24
<>0.00 B2009-10-07 18:54
temper.R2.62 kB2009-09-20 17:21
initseq.R136.00 B2009-08-28 12:24
olbm.R601.00 B2009-08-28 12:24
metrop.R2.12 kB2009-10-07 18:54
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Markov Chain Monte Carlo stochastic differential equations (578.94 kB)

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