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34th IMAC Conference and Exposition on Structural Dynamics

作者： Chowdhury, Asif Terejanu, Gabriel Univ South Carolina Dept Comp Sci & Engn 315 Main St Columbia SC 29208 USA

ISBN: (纸本)9783319297545;9783319297538

Markov Chain Monte Carlo (MCMC) has become the main computational workhorse in scientific computing for solving statistical inverse problems. It is difficult however to use MCMC algorithms when the likelihood function is computational expensive to evaluate. The status quo tackles this problem by emulating the computationally models upfront based on a number of forward simulations, and then the emulator is used in the MCMC simulation instead of the actual model. However, this strategy has an obvious drawback: how should one choose the initial number of forward simulations to capture the complexity of the model response? This is the main driver question that this paper tries to answer. Here, a novel metropolis-hastings algorithm is proposed to sample from posterior distributions corresponding to computationally expensive simulations. The main innovation is emulating the likelihood function using Gaussian processes. The proposed emulator is constructed on the fly as the MCMC simulation evolves and adapted based on the uncertainty in the acceptance rate. The algorithm is tested on a number of benchmark problems where it is shown that it significantly reduces the number of forward simulations.

关键词： Gaussian process Markov chain Monte Carlo metropolis-hastings algorithm Likelihood function

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Studies of Mutation Accumulation in Three Codon Positions using Monte Carlo Simulations and metropolis-hastings algorithm

Studies of Mutation Accumulation in Three Codon Positions us...

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5th International Conference on Bioinformatics Models, Methods and algorithms (BIOINFORMATICS) is part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Grabinska, Malgorzata Blazej, Pawel Mackiewicz, Pawel Univ Wroclaw Dept Genom Fac Biotechnol PL-50138 Wroclaw Poland

ISBN: (纸本)9789897580123

Protein coding sequences are characterized by specific nucleotide composition in three codon positions as a result of mutational and selection pressures. To analyse the impact of mutations and different transi-tion/ transversion ratio on three codon position in protein coding sequences, we elaborated a model of genome evolution based Monte Carlo simulation. Selection was applied against stop translation codons and modified metropolis-hastings algorithm to maintain typical nucleotide composition of particular codon positions. The simulations were performed on genomes consisting of bacterial gene sequences. We used a series of nucleotide substitution matrices assuming different transition/ transversion ratio and nucleotide stationary distribution characteristic of the real mutational pressure. The simulations showed exponential decrease in the number of eliminated genomes with the growth of the transition/ transversion ratio. The same trend was also observed both for accepted and to lesser extent for rejected mutations. The third codon positions much more mutations accepted than rejected because of very similar composition to the mutational stationary distribution, whereas the first positions accumulated the smallest number of mutations and rejected the most as a result of strong selection on its nucleotide composition. The obtained results showed different response of three codon positions on mutational pressure related with their characteristic nucleotide composition.

关键词： Codon Evolution metropolis-hastings algorithm Monte Carlo Simulations Mutation Nucleotide Composition Protein Coding Sequence Rate Matrix Selection Substitution Transition Transversion

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Performance Analysis of Generalized metropolis-hastings algorithm over Mobile Wireless Sensor Networks 30

Performance Analysis of Generalized Metropolis-Hastings Algo...

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30th International Conference on Cybernetics and Informatics (K and I)

作者： Kenyeres, Martin Kenyeres, Jozef Slovak Acad Sci Inst Informat Bratislava Slovakia Sipwise GmbH Brunn Austria

ISBN: (纸本)9781728143811

In the last years, we could have witnessed significant progress in wireless sensor networks in terms of many aspects such as hardware components, communication and routing protocols, etc. Lately, the attention of both the academy and the industry has been more and more focused on the mobile variant of this technology. Even though mobility brings many advantages, a lot of challenges related to sensor networks are still not solved. In this paper, we address the generalized metropolis-hastings algorithm for distributed averaging, whose goal is to overcome negative factors affecting the sensor readings as well as suppressing data redundancy. We test the algorithm with a varied mixing parameter over stationary edge-Markovian evolving graphs in order to identify the optimal configuration of the analyzed algorithm.

关键词： Mobile wireless sensor networks metropolis-hastings algorithm data aggregation

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Bayesian Analysis of Mixture Normal Model via Equi-Energy Sampler and Improved metropolis-hastings algorithm 7

Bayesian Analysis of Mixture Normal Model via Equi-Energy Sa...

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6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Shao, Wei Zhao, Guoqing Shao, Feifei Qufu Normal Univ Sch Management Rizhao 276826 Peoples R China Shandong Univ Finance & Econ Sch Finance Jinan 250014 Peoples R China Linyi Vocat Coll Dept Engn Linyi 276017 Peoples R China

ISBN: (纸本)9781467392006

Mixture normal model provides a convenient and flexible probabilistic representation of heterogeneous data, and the estimation of parameters received considerable attention in recent years. In this paper, we propose a Bayesian analysis of mixture normal model. Because the the posterior probability density function is too complicated to be used to draw samples directly using standard Markov Chain Monte Carlo method, we use two method, the Improved metropolis-hastings algorithm and Equi-energy sampler, to conquer the drawback. We show by numerical simulations that both Equi-energy sampler and Improved metropolis-hastings algorithm outperform the standard metropolis-hastings algorithm.

关键词： Bayesian analysis Mixture model Equi-energy sampler metropolis-hastings algorithm Markov Chain Monte Carlo

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Modified metropolis-hastings algorithm for Efficient Sampling from Discrete Probability Distributions (MMH-DPD) Applied to Field Operational Tests database (SPMD) 3

Modified Metropolis-Hastings Algorithm for Efficient Samplin...

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3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Chelbi, Nacer Eddine Gingras, Denis Sauvageau, Claude Univ Sherbrooke LIV Sherbrooke PQ Canada PMG Technol Inc 100 Rue Landais Blainville PQ Canada

ISBN: (纸本)9781538678312

metropolis-hastings algorithm (MH) is the most popular Markov Chain Monte Carlo (MCMC) method. Essentially, the MH algorithm generates a sample, accepts or rejects the sample based on an acceptance probability that is related to the continuous target probability distribution. In this work, we propose a modified metropolis-hastings algorithm (MMH-DPD) that can draw samples from discrete probability distributions. For starters, the discrete probability distribution is replaced with a multimodal distribution and a new step after the rejection and acceptation step is added to the original algorithm. To reduce the error caused by the tail of the multimodal distribution, we used a mixture of Generalized Gaussians instead. Numerical results and a generalization of the proposed algorithm are provided. Our simulations show that the proposed sampler reliably creates a Markov chain that generates a sequence of values, in such a way that as the number of samples goes to infinity, we can guarantee that they reflect samples from the target discrete distribution.

关键词： Markov Chain Monte Carlo (MCMC) metropolis-hastings algorithm mixture of Generalized Gaussians discrete probability distribution Field Operational Tests SPMD

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A metropolis-hastings algorithm for Task Allocation 60

A Metropolis-Hastings Algorithm for Task Allocation

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60th IEEE Conference on Decision and Control (CDC)

作者： Hamza, Doha Toonsi, Sarah Shamma, Jeff S. King Abdullah Univ Sci & Technol KAUST CEMSE Div Thuwal Saudi Arabia Univ Illinois Ind & Enterprise Syst Engn ISE Urbana IL USA

ISBN: (纸本)9781665436595

We consider a robot-location assignment problem. The problem is confounded by a location network that restricts robots' motion to neighboring locations. The problem can be optimally solved using a centralized Hungarian algorithm. We propose a distributed game-theoretic algorithm, based on the metropolis-hastings mechanism, to eliminate the need for a central coordinator. Agents are activated at random. Agents compare their action, location, against a neighboring one based on the location network. If the new location represents an improvement in agent's utility, they move with probability one, otherwise, they move with a probability proportional to the difference in the two locations' utilities. Our algorithm converges to the optimal assignment of robots to locations. We provide extensive simulations, compare with previous work, and demonstrate the versatility of the proposed algorithm to various task allocation scenarios.

关键词： Task allocation game-theoretic algorithm distributed learning metropolis-hastings algorithm

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Inference of gene regulatory network by Bayesian network using metropolis-hastings algorithm 1

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3rd International Conference on Advanced Data Mining and Applications

作者： Kirimasthong, Khwunta Manorat, Aompilai Chaijaruwanich, Jeerayut Prasitwattanaseree, Sukon Thammarongtham, Chinae Chiang Mai Univ Biomed Engn Ctr Fac Sci Dept Comp Sci Chiang Mai 50200 Thailand Chiang Mai Univ Fac Sci Dept Stat Chiang Mai 50200 Thailand Natl Ctr Genet Engn & Biotechnol Pathum Thani 12120 Thailand

ISBN: (数字)9783540738718

ISBN: (纸本)9783540738701

Bayesian networks are widely used to infer genes regulatory network from their transcriptional expression data. Bayesian network of the best score is usually chosen as genes regulatory model. However, without the hint from biological ground truth, and given a small number of transcriptional expression observations, the resulting Bayesian networks might not correspond to the real one. To deal with these two constrains, this paper proposes a stochastic approach to fit an existing hypothetical gene regulatory network, derived from biological evidence, with few available amount of transcriptional expression levels of the genes. The hypothetical gene regulatory network is set as an initial model of Bayesian network and fitted with transcriptional expression data by using metropolis-hastings algorithm. In this work, the transcriptional regulation of gene CYC1 by co-regulators HAP2 HAP3 HAP4 of yeast (Saccharomyces Cerevisiae) is considered as example. Due to the simulation results, ten probable gene regulatory networks which are similar to the given hypothetical model are obtained. This shows that metropolis-hastings algorithm can be used as a simulation model for gene regulatory network.

关键词： bayesian network gene regulatory network metropolis-hastings algorithm transcriptional expression analysis

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Note on the sampling distribution for the metropolis-hastings algorithm

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2003年第4期32卷 775-789页

作者： Geweke, J Tanizaki, H Kobe Univ Grad Sch Econ Kobe Hyogo 6578501 Japan Univ Iowa Dept Econ Iowa City IA 52242 USA

The metropolis-hastings algorithm has been important in the recent development of Bayes methods. This algorithm generates random draws from a target distribution utilizing a sampling (or proposal) distribution. This article compares the properties of three sampling distributions-the independence chain, the random walk chain, and the Taylored chain suggested by Geweke and Tanizaki (Geweke, J., Tanizaki, H. (1999). On Markov Chain Monte-Carlo methods for nonlinear and non-Gaussian state-space models. Communications in Statistics, Simulation and. Computation 28(4):867-894, Geweke, J., Tanizaki, H. (2001). Bayesian estimation of state-space model using the metropolis-hastings algorithm within Gibbs sampling. Computational Statistics and Data Analysis 37(2):151-170).

关键词： metropolis-hastings algorithm sampling distribution taylored chain

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Density estimation for the metropolis-hastings algorithm

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SCANDINAVIAN JOURNAL OF STATISTICS 2003年第4期30卷 699-718页

作者： Sköld, M Roberts, GO Lund Univ Ctr Math Sci SE-22100 Lund Sweden Univ Lancaster Lancaster LA1 4YW England

Kernel density estimation is an important tool in visualizing posterior densities from Markov chain Monte Carlo output. It is well known that when smooth transition densities exist, the asymptotic properties of the estimator agree with those for independent data. In this paper, we show that because of the rejection step of the metropolis-hastings algorithm, this is no longer true and the asymptotic variance will depend on the probability of accepting a proposed move. We find an expression for this variance and apply the result to algorithms for automatic bandwidth selection.

关键词： density estimation metropolis-hastings algorithm

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Geometric convergence of the metropolis-hastings simulation algorithm

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STATISTICS & PROBABILITY LETTERS 1998年第4期39卷 371-377页

作者： Holden, L Norwegian Comp Ctr N-0314 Oslo Norway Univ Oslo N-0314 Oslo Norway

Necessary and sufficient conditions for uniform geometric convergence in the relative supremum norm of the metropolis-hastings simulation algorithm with a general generating function are established. An explicit expre... 详细信息

关键词： metropolis-hastings algorithm convergence

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