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Authors
(11086)
Arnaud Doucet
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Yasuhiro Omori (大森裕浩)
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Keywords
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Keywords
Markov Chain Monte Carlo
MCMC,Markov Chain Monte Carlo,Markov Chains Monte Carlo
Markov Chain Monte Carlo  MCMC
Publications: 7,072

Citation Count: 75,088
Stemming Variations:
Markov Chains Monte Carlo
Cumulative
Annual
Definition Context
(5)
Markov chain Monte Carlo (McMC) simulation is a popular computational tool for making inferences from complex, highdimensional probability densities. Given a particular target density , the idea behind this technique is to simulate a Markov chain that has as its stationary distribution...
T. Hermann
,
et al.
SONIFICATION OF MARKOV CHAIN MONTE CARLO SIMULATIONS
Markov chain Monte Carlo (MCMC) is a popular class of algorithms to sample from a complex distribution. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behaviour. This has led some authors to propose the use of a population of MCMC chains, while others go even further by integrating techniques from evolutionary computation (EC) into the MCMC framework...
Madalina M. Drugan
,
et al.
Evolutionary Markov Chain Monte Carlo
Markov chain Monte Carlo (MCMC) is a methodology that is gaining widespread use in the phylogenetics community and is central to phylogenetic software packages such as MrBayes. An important issue for users of MCMC methods is how to select appropriate values for adjustable parameters such as the length of the Markov chain or chains, the sampling density, the proposal mechanism, and, if Metropoliscoupled MCMC is being used, the number of heated chains and their temperatures...
ROBERT G. BEIKO
,
et al.
Searching for Convergence in Phylogenetic Markov Chain Monte Carlo
Markov Chain Monte Carlo (MCMC) is a computerintensive statistical tool that has received considerable attention over the past few years. Using MCMC theory, it is often quite simple to write efficient algorithms for sampling from extremely complicated target distributions; thus, it is not difficult to understand why these techniques have found important applications in a vast number of different areas. Although the literature on MCMC methods is growing rapidly, the excellent book by Gilks, Richardson and Spiegelhalter (1996) provides a good starting point for the interested reader...
Håkan Andersson
,
et al.
Markov Chain Monte Carlo
Markov chain Monte Carlo (MCMC) is an important computational technique for generating samples from nonstandard probability distributions. A major challenge in the design of practical MCMC samplers is to achieve efficient convergence and mixing properties...
Jonathan M. Keith
,
et al.
Adaptive independence samplers
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Publications
(7072)
Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures
Konstantinos Bousmalis
,
Stefanos Zafeiriou
,
LouisPhilippe Morency
,
Maja Pantic
,
Zoubin Ghahramani
Published in 2013.
Infinite Hidden Conditional Random Fields for Human Behavior Analysis
Konstantinos Bousmalis
,
Stefanos Zafeiriou
,
LouisPhilippe Morency
,
Maja Pantic
Published in 2013.
Using Equilibrium Policy Gradients for Spatiotemporal Planning in Forest Ecosystem Management
Mark Crowley
Published in 2013.
Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations
(
Citations: 4
)
Andriy Norets
Journal:
Econometric Reviews  ECONOM REV
, vol. 31, no. 1, pp. 84106, 2012
One and TwoSample Bayesian Prediction Intervals Based on TypeI Hybrid Censored Data
(
Citations: 1
)
A. R. Shafay
,
N. Balakrishnan
Journal:
Communications in Statisticssimulation and Computation  COMMUN STATISTSIMULAT COMPUT
, vol. 41, no. 1, pp. 6588, 2012