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Page 652 of Mathematical Reviews Vol. 58, Issue 1 [page]

1979 Mathematical Reviews  
For the construction of these approximations he uses the idea that the Bayesian prob- lem can be transformed to a standard Markov decision problem by incorporating the posterior distribution in the state  ...  They prove that contracting Markov decision processes, in the sense of J. A. van Nunen [Contracting Markov decision processes, Math. Centr., Amsterdam 1976; MR 58 #20474], fit into this framework.  ... 

Air Traffic Forecast Empirical Research Based on the MCMC Method

Jian-bo Wang, Chong-jun Fan, Lei Bai
2012 Computer and Information Science  
In this paper, the Markov Chain Monte Carlo (MCMC) method of applied theory of statistics has been introduced into the aviation sector, and the discussion on airport air traffic forecast has been conducted  ...  Airport air traffic is one of the most important and hardest one among all the airport data forecasts.  ...  The basic idea of MCMC method is to construct a Markov Chain, making its stationary distribution the posterior distribution of the parameters to be estimated, thus generating the samples of posterior distribution  ... 
doi:10.5539/cis.v5n5p50 fatcat:agt3hvr2svas3fcufcche75trm

Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition [chapter]

J. Denzler, C.M. Brown, H. Niemann
2001 Lecture Notes in Computer Science  
The results show that the sequential decision process outperforms a random strategy, both in the sense of recognition rate and number of views necessary to return a decision.  ...  The convergence of the decision process can be proven. We demonstrate the benefits of our approach using an active object recognition scenario.  ...  The key point of the convergence proof is that a irreducible Markov chain can be defined representing the sequential decision process [4] . Two corrolaries give us the proof of convergence.  ... 
doi:10.1007/3-540-45404-7_41 fatcat:v3jjled6ibebzj4bdwwfk6rtnu

Convergence Monitoring of Markov Chains Generated for Inverse Tracking of Unknown Model Parameters in Atmospheric Dispersion

Joo Yeon KIM, Hyung Joon RYU, Gyu Hwan JUNG, Jai Ki LEE
2011 Progress in Nuclear Science and Technology  
These two diagnostics have been applied for the posterior quantities of the release point and the release rate inferred through the inverse tracking of unknown model parameters for the Yonggwang atmospheric  ...  From these two convergence diagnostics, the validation of Markov chains generated have been ensured and PSRF then is especially suggested as the efficient tool for convergence monitoring for the source  ...  Acknowledgment This work was supported by Korean Ministry of Knowledge Economy (2008-P-EP-HM-E-06-0000) and Sunkwang Atomic Energy Safety Co., Ltd..  ... 
doi:10.15669/pnst.1.464 fatcat:7mbib4m3nbdmti6lni6clvxxze

Implementing random scan Gibbs samplers

Richard A. Levine, Zhaoxia Yu, William G. Hanley, John J. Nitao
2005 Computational statistics (Zeitschrift)  
The Gibbs sampler, being a popular routine amongst Markov chain Monte Carlo sampling methodologies, has revolutionized the application of Monte Carlo methods in statistical computing practice.  ...  The decision rules through which this strategy is chosen are based on convergence properties of the induced chain and precision of statistical inferences drawn from the generated Monte Carlo samples.  ...  We also study the effect of decision criteria, be it convergence rate or variance, on the choice of visitation strategy.  ... 
doi:10.1007/bf02736129 fatcat:eqs7jjeqljefdkisjukysegfre

Dynamic Tempered Transitions for Exploring Multimodal Posterior Distributions

Jeff Gill, George Casella
2004 Political Analysis  
Multimodal, high-dimension posterior distributions are well known to cause mixing problems for standard Markov chain Monte Carlo (MCMC) procedures; unfortunately such functional forms readily occur in  ...  in response to current posterior features.  ...  A Markov chain has converged to its limiting distribution (the posterior of interest for properly setup MCMC applications) when it generates only legitimate values from this distribution in proportion  ... 
doi:10.1093/pan/mph027 fatcat:kabu7eckxzeq3g3pftrmrkd7c4

Is Partial-Dimension Convergence a Problem for Inferences from MCMC Algorithms?

Jeff Gill
2008 Political Analysis  
The usual culprit is slow mixing of the Markov chain and therefore slow convergence towards the target distribution.  ...  Although practitioners are generally aware of the importance of convergence of the Markov chain, many are not fully aware of the difficulties in fully assessing convergence across multiple dimensions.  ...  A Markov chain has converged at time t to its invariant distribution (the posterior distribution of interest for correctly setup Bayesian applications) when the transition kernel produces draws arbitrarily  ... 
doi:10.1093/pan/mpm019 fatcat:75u6fnbhxjblbagvszri7mwypa

Convergence analyses and comparisons of Markov chain Monte Carlo algorithms in digital communications

Rong Chen, J.S. Liu, Xiaodong Wang
2002 IEEE Transactions on Signal Processing  
nor do they explicitly estimate the channel by employing training signals or decision-feedback; and c) they are well suited for iterative (turbo) processing in coded systems.  ...  Recently, Markov chain Monte Carlo (MCMC) methods have been applied to the design of blind Bayesian receivers in a number of digital communications applications.  ...  CONVERGENCE OF MCMC SAMPLERS In all MCMC algorithms, a Markov transition rule (or kernel) is first constructed so that its limiting distribution is the desired posterior distribution.  ... 
doi:10.1109/78.978381 fatcat:nkgpgxiz7rgbtkjyzrijxrcfhy

A simple introduction to Markov Chain Monte–Carlo sampling

Don van Ravenzwaaij, Pete Cassey, Scott D. Brown
2016 Psychonomic Bulletin & Review  
Markov Chain Monte-Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference.  ...  Keywords Markov Chain Monte-Carlo · MCMC · Bayesian inference · Tutorial Over the course of the twenty-first century, the use of Markov chain Monte-Carlo sampling, or MCMC, has grown dramatically.  ...  The process of ignoring the initial part of the Markov chain is discussed in more detail later in this section.  ... 
doi:10.3758/s13423-016-1015-8 pmid:26968853 pmcid:PMC5862921 fatcat:3oqyp5bphvfcxocsvdqmf6jqsu

Bridge Deterioration Modeling by Markov Chain Monte Carlo (MCMC) Simulation Method [chapter]

N. K. Walgama Wellalage, Tieling Zhang, Richard Dwight, Khaled El-Akruti
2014 Lecture Notes in Mechanical Engineering  
Results show that TPMs corresponding to critical bridge elements can be obtained by Metropolis-Hasting Algorithm (MHA) coded in MATLAB program until it converges to stationary transition probability distributions  ...  Results show that TPMs corresponding to critical bridge elements can be obtained by Metropolis-Hasting Algorithm (MHA) coded in MATLAB program until it converges to stationary transition probability distributions  ...  P(θ /Y) is known as the posterior distribution or target distribution and P(θ) is called prior distribution of unknown model parameter.  ... 
doi:10.1007/978-3-319-09507-3_47 fatcat:v2bvmdgdabam3kcdz4ry3x4o4y

Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

Yi-Shin Lin, Luke Strickland
2020 The Quantitative Methods for Psychology  
Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence.  ...  We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets.  ...  The right panel shows the Markov chains are well-mixed. The lower panel in Figure 4 shows the posterior distributions of each parameter.  ... 
doi:10.20982/tqmp.16.2.p133 fatcat:6vuhbrq44raijhd3qrgq3gyzgu

Inferring the Optimal Policy using Markov Chain Monte Carlo [article]

Brandon Trabucco, Albert Qu, Simon Li, Ganeshkumar Ashokavardhanan
2019 arXiv   pre-print
In order to resolve these problems, we propose a technique using Markov Chain Monte Carlo to generate samples from the posterior distribution of the parameters conditioned on being optimal.  ...  This paper investigates methods for estimating the optimal stochastic control policy for a Markov Decision Process with unknown transition dynamics and an unknown reward function.  ...  In Neal's formulation, the posterior distributions of the parameters θ given the dataset of inputs and labels D is approximated using Markov Chain Monte Carlo [7] .  ... 
arXiv:1912.02714v1 fatcat:mflokdp2kjda7mvrlpdc7z4sxi

Theory and Dynamics of Perceptual Bistability [chapter]

2007 Advances in Neural Information Processing Systems 19  
We formalize the theory, explicitly derive switching rate distributions and discuss qualitative properties of the theory including the effect of changes in the posterior distribution on switching rates  ...  In particular, we propose that the brain explores a posterior distribution over image interpretations at a rapid time scale via a sampling-like process and updates its interpretation when a sampled interpretation  ...  Extrema of the posterior sampling process If the sampling process has no long-range temporal dependence, then under mild assumptions the distribution of extrema converge in distribution 5 to one of three  ... 
doi:10.7551/mitpress/7503.003.0157 fatcat:ceuproo4lvaxbkst2ob4elqyme

Page 7283 of Mathematical Reviews Vol. , Issue 2000j [page]

2000 Mathematical Reviews  
They show that the optimal rate of convergence to a normal distribution for # is not obtained unless g is undersmoothed.  ...  For each mode, the optimal rate of convergence (o.r.c.) for i.i.d. sequences is known.  ... 

Iterative simulation methods

B.D. Ripley, M.D. Kirkland
1990 Journal of Computational and Applied Mathematics  
The standard methods of generating sample from univariate distributions often become hopelessly inefficient when applied to realizations of stochastic processes .  ...  The methods are surveyed and compared, with particular reference to their convergence properties .  ...  Both the prior distribution and the model of the observation process are arranged carefully so that the posterior is a Markov random field on a sparsely connected graph .  ... 
doi:10.1016/0377-0427(90)90347-3 fatcat:wd6foqhdwnepfpuuv24rkkzjiu
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