Relevant bibliographies by topics / MCMC algoritmus

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  1. Journal articles
  2. Dissertations / Theses
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Academic literature on the topic 'MCMC algoritmus'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "MCMC algoritmus"

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Drugan, Mădălina M., and Dirk Thierens. "Geometrical Recombination Operators for Real-Coded Evolutionary MCMCs." Evolutionary Computation 18, no. 2 (2010): 157–98. http://dx.doi.org/10.1162/evco.2010.18.2.18201.

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Markov chain Monte Carlo (MCMC) algorithms are sampling methods for intractable distributions. In this paper, we propose and investigate algorithms that improve the sampling process from multi-dimensional real-coded spaces. We present MCMC algorithms that run a population of samples and apply recombination operators in order to exchange useful information and preserve commonalities in highly probable individual states. We call this class of algorithms Evolutionary MCMCs (EMCMCs). We introduce and analyze various recombination operators which generate new samples by use of linear transformation
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Liang, Faming, and Ick-Hoon Jin. "A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants." Neural Computation 25, no. 8 (2013): 2199–234. http://dx.doi.org/10.1162/neco_a_00466.

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Simulating from distributions with intractable normalizing constants has been a long-standing problem in machine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo estimate in simulations, while still converges, as shown in the letter, to the desired target distribution under mild conditions. The MCMH algorithm is illustrated with spatial autologistic models and e
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Robert, Christian P., Víctor Elvira, Nick Tawn, and Changye Wu. "Accelerating MCMC algorithms." Wiley Interdisciplinary Reviews: Computational Statistics 10, no. 5 (2018): e1435. http://dx.doi.org/10.1002/wics.1435.

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Holden, Lars. "Mixing of MCMC algorithms." Journal of Statistical Computation and Simulation 89, no. 12 (2019): 2261–79. http://dx.doi.org/10.1080/00949655.2019.1615064.

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Papaioannou, Iason, Wolfgang Betz, Kilian Zwirglmaier, and Daniel Straub. "MCMC algorithms for Subset Simulation." Probabilistic Engineering Mechanics 41 (July 2015): 89–103. http://dx.doi.org/10.1016/j.probengmech.2015.06.006.

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Nguyen, Dao, Perry de Valpine, Yves Atchade, Daniel Turek, Nicholas Michaud, and Christopher Paciorek. "Nested Adaptation of MCMC Algorithms." Bayesian Analysis 15, no. 4 (2020): 1323–43. http://dx.doi.org/10.1214/19-ba1190.

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Rosenthal, Jeffrey S., and Jinyoung Yang. "Ergodicity of Combocontinuous Adaptive MCMC Algorithms." Methodology and Computing in Applied Probability 20, no. 2 (2017): 535–51. http://dx.doi.org/10.1007/s11009-017-9574-3.

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Browne, William J. "MCMC algorithms for constrained variance matrices." Computational Statistics & Data Analysis 50, no. 7 (2006): 1655–77. http://dx.doi.org/10.1016/j.csda.2005.02.008.

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Sinharay, Sandip. "Experiences With Markov Chain Monte Carlo Convergence Assessment in Two Psychometric Examples." Journal of Educational and Behavioral Statistics 29, no. 4 (2004): 461–88. http://dx.doi.org/10.3102/10769986029004461.

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There is an increasing use of Markov chain Monte Carlo (MCMC) algorithms for fitting statistical models in psychometrics, especially in situations where the traditional estimation techniques are very difficult to apply. One of the disadvantages of using an MCMC algorithm is that it is not straightforward to determine the convergence of the algorithm. Using the output of an MCMC algorithm that has not converged may lead to incorrect inferences on the problem at hand. The convergence is not one to a point, but that of the distribution of a sequence of generated values to another distribution, an
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Karagiannis, Georgios, and Christophe Andrieu. "Annealed Importance Sampling Reversible Jump MCMC Algorithms." Journal of Computational and Graphical Statistics 22, no. 3 (2013): 623–48. http://dx.doi.org/10.1080/10618600.2013.805651.

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Dissertations / Theses on the topic "MCMC algoritmus"

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Hrbek, Filip. "Metody předvídání volatility." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264689.

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In this masterthesis I have rewied basic approaches to volatility estimating. These approaches are based on classical and Bayesian statistics. I have applied the volatility models for the purpose of volatility forecasting of a different foreign exchange (EURUSD, GBPUSD and CZKEUR) in the different period (from a second period to a day period). I formulate the models EWMA, GARCH, EGARCH, IGARCH, GJRGARCH, jump diffuison with constant volatility and jump diffusion model with stochastic volatility. I also proposed an MCMC algorithm in order to estimate the Bayesian models. All the models we estim
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Austad, Haakon Michael. "Parallel Multiple Proposal MCMC Algorithms." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12857.

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We explore the variance reduction achievable through parallel implementation of multi-proposal MCMC algorithms and use of control variates. Implemented sequentially multi-proposal MCMC algorithms are of limited value, but they are very well suited for parallelization. Further, discarding the rejected states in an MCMC sampler can intuitively be interpreted as a waste of information. This becomes even more true for a multi-proposal algorithm where we discard several states in each iteration. By creating an alternative estimator consisting of a linear combination of the traditional sample mean a
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Thiéry, Alexandre H. "Scaling analysis of MCMC algorithms." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/57609/.

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Markov Chain Monte Carlo (MCMC) methods have become a workhorse for modern scientific computations. Practitioners utilize MCMC in many different areas of applied science yet very few rigorous results are available for justifying the use of these methods. The purpose of this dissertation is to analyse random walk type MCMC algorithms in several limiting regimes that frequently occur in applications. Scaling limits arguments are used as a unifying method for studying the asymptotic complexity of these MCMC algorithms. Two distinct strands of research are developed: (a) We analyse and prove diffu
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Melo, Ana Cláudia Oliveira de. "Aspectos Práticos Computacionais dos Algoritmos de Simulação MCMC." Universidade de São Paulo, 1999. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05032018-163433/.

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Os algoritmos de simulação de Monte Carlo em cadeia de Markov (MCMC) têm aplicações em várias áreas da Estatística, entre elas destacamos os problemas de Inferência Bayesiana. A aplicação destas técnicas no entanto, exige uma análise teórica da distribuição a posteriori para assegurar a convergência. Devido ao alto grau de complexidade de certos problemas, essa análise nem sempre é possível. O objetivo deste estudo é destacar estas dificuldades e apresentar alguns aspectos práticos computacionais que podem auxiliar na solução de problemas de inferência Bayesiana. Entre estes ressaltamos os cri
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Altaleb, Anas. "Méthodes d'échantillonnage par mélanges et algorithmes MCMC." Rouen, 1999. http://www.theses.fr/1999ROUES034.

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Nous abordons dans cette thèse, deux aspects distincts : (a) la construction et le test de certaines méthodes de simulation pour l'approximation des intégrales. Nous étudions en particulier les estimateurs de Monte Carlo auxquels il est souvent fait appel dans le traitement de modèles statistiques complexes. Notre apport en ce domaine consiste en l'utilisation des mélanges pour la stabilisation des échantillonnages d'importance. Pour valider l'estimateur pondéré, il est indispensable d'étudier son comportement pour les méthodes MCMC qui permettent la mise en œuvre d'une forme généralisée de l'
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Liu, Shuanglong. "Acceleration of MCMC-based algorithms using reconfigurable logic." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/52431.

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Monte Carlo (MC) methods such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) have emerged as popular tools to sample from high dimensional probability distributions. Because these algorithms can draw samples effectively from arbitrary distributions in Bayesian inference problems, they have been widely used in a range of statistical applications. However, they are often too time consuming due to the prohibitive costly likelihood evaluations, thus they cannot be practically applied to complex models with large-scale datasets. Currently, the lack of sufficiently fast MCMC met
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Medina, Aguayo Felipe Javier. "Stability and examples of some approximate MCMC algorithms." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/88922/.

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Approximate Monte Carlo algorithms are not uncommon these days, their applicability is related to the possibility of controlling the computational cost by introducing some noise or approximation in the method. We focus on the stability properties of a particular approximate MCMC algorithm, which we term noisy Metropolis-Hastings. Such properties have been studied before in tandem with the pseudo-marginal algorithm, but under fairly strong assumptions. Here, we examine the noisy Metropolis-Hastings algorithm in more detail and explore possible corrective actions for reducing the introduced bias
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Mingas, Grigorios. "Algorithms and architectures for MCMC acceleration in FPGAs." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/31572.

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Markov Chain Monte Carlo (MCMC) is a family of stochastic algorithms which are used to draw random samples from arbitrary probability distributions. This task is necessary to solve a variety of problems in Bayesian modelling, e.g. prediction and model comparison, making MCMC a fundamental tool in modern statistics. Nevertheless, due to the increasing complexity of Bayesian models, the explosion in the amount of data they need to handle and the computational intensity of many MCMC algorithms, performing MCMC-based inference is often impractical in real applications. This thesis tackles this com
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Chang, Meng-I. "A Comparison of Two MCMC Algorithms for Estimating the 2PL IRT Models." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1446.

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The fully Bayesian estimation via the use of Markov chain Monte Carlo (MCMC) techniques has become popular for estimating item response theory (IRT) models. The current development of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been used with fitting various IRT models, the latter is relatively new, calling for the research to compare it with other algorithms. The purpose of the present study is to evaluate the performances of these two emerging MCMC algorithms in estimating two two-parameter logistic (2PL) IRT models, namely, the 2
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Faure, Charly. "Approches bayésiennes appliquées à l’identification d’efforts vibratoires par la méthode de Résolution Inverse." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1002.

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Des modèles de plus en plus précis sont développés pour prédire le comportement vibroacoustique des structures et dimensionner des traitements adaptés. Or, les sources vibratoires, qui servent de données d'entrée à ces modèles, restent assez souvent mal connues. Une erreur sur les sources injectées se traduit donc par un biais sur la prédiction vibroacoustique. En amont des simulations, la caractérisation expérimentale de sources vibratoires en conditions opérationnelles est un moyen de réduire ce biais et fait l'objet de ces travaux de thèse.L'approche proposée utilise une méthode inverse, la
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Books on the topic "MCMC algoritmus"

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Graphs with hard constraints: further applications and extensions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0007.

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This chapter looks at further topics pertaining to the effective use of Markov Chain Monte Carlo to sample from hard- and soft-constrained exponential random graph models. The chapter considers the question of how moves can be sampled efficiently without introducing unintended bias. It is shown mathematically and numerically that apparently very similar methods of picking out moves can give rise to significant differences in the average topology of the networks generated by the MCMC process. The general discussion in complemented with pseudocode in the relevant section of the Algorithms chapte
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Book chapters on the topic "MCMC algoritmus"

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Robert, Christian P., and Dominique Cellier. "Convergence Control of MCMC Algorithms." In Discretization and MCMC Convergence Assessment. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1716-9_2.

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Lundén, Daniel, Johannes Borgström, and David Broman. "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages." In Programming Languages and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_15.

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AbstractProbabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing research on such algorithms mainly concerns their implementation and efficiency, rather than the correctness of the algorithms themselves when applied in the context of expressive PPLs. To remedy this, we give a correctness proof for SMC methods in the context of an expressive PPL calculus, representative of popular PPLs such as WebPPL, Anglican, and Birch. Previous work have studied correctness of MCMC using an operational semantics, and correctness of SMC and MCMC in a denotational setting without term recursion. However, for SMC inference—one of the most commonly used algorithms in PPLs as of today—no formal correctness proof exists in an operational setting. In particular, an open question is if the resample locations in a probabilistic program affects the correctness of SMC. We solve this fundamental problem, and make four novel contributions: (i) we extend an untyped PPL lambda calculus and operational semantics to include explicit resample terms, expressing synchronization points in SMC inference; (ii) we prove, for the first time, that subject to mild restrictions, any placement of the explicit resample terms is valid for a generic form of SMC inference; (iii) as a result of (ii), our calculus benefits from classic results from the SMC literature: a law of large numbers and an unbiased estimate of the model evidence; and (iv) we formalize the bootstrap particle filter for the calculus and discuss how our results can be further extended to other SMC algorithms.
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Robert, Christian P., and George Casella. "Convergence Monitoring and Adaptation for MCMC Algorithms." In Introducing Monte Carlo Methods with R. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1576-4_8.

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Liu, Xianghua, Liuling Li, and Hiroki Tsurumi. "Bayesian Inference of Financial Models Using MCMC Algorithms." In Handbook of Quantitative Finance and Risk Management. Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-77117-5_91.

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Robert, Christian P., and George Casella. "Contrôle de convergence et adaptation des algorithmes MCMC." In Méthodes de Monte-Carlo avec R. Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_8.

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Mneimneh, Saad, and Syed Ali Ahmed. "Gibbs/MCMC Sampling for Multiple RNA Interaction with Sub-optimal Solutions." In Algorithms for Computational Biology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38827-4_7.

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Bhatnagar, Nayantara, Andrej Bogdanov, and Elchanan Mossel. "The Computational Complexity of Estimating MCMC Convergence Time." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22935-0_36.

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Sherwani, Naveed. "Physical Design Automation of MCMs." In Algorithms for VLSI Physical Design Automation. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2351-2_12.

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Sherwani, Naveed A. "Physical Design Automation of MCMs." In Algorithms for VLSI Physical Design Automation. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4757-2219-2_12.

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Hernandez-Marin, Sergio, Andrew M. Wallace, and Gavin J. Gibson. "Creating Multi-layered 3D Images Using Reversible Jump MCMC Algorithms." In Advances in Visual Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919629_42.

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Conference papers on the topic "MCMC algoritmus"

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Martino, Luca, Victor Elvira, David Luengo, Antonio Artes-Rodriguez, and Jukka Corander. "Orthogonal MCMC algorithms." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884651.

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Andrieu, Christophe, and Y. F. Atchadé. "On the efficiency of adaptive MCMC algorithms." In the 1st international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1190095.1190150.

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Huang, Lingxiao, Pinyan Lu, and Chihao Zhang. "Canonical Paths for MCMC: from Art to Science." In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974331.ch38.

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Mingas, Grigorios, Farhan Rahman, and Christos-Savvas Bouganis. "On Optimizing the Arithmetic Precision of MCMC Algorithms." In 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2013. http://dx.doi.org/10.1109/fccm.2013.31.

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Marnissi, Yosra, Emilie Chouzenoux, Jean-Christophe Pesquei, and Amel Benazza-Benyahia. "An auxiliary variable method for Langevin based MCMC algorithms." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551764.

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Wentworth, Mami T., and Ralph C. Smith. "Construction of Bayesian Prediction Intervals for Smart Systems." In ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/smasis2013-3168.

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In this paper, we employ adaptive Metropolis algorithms to construct densities for parameters and quantities of interest for models arising in the analysis of smart material structures. In the first step of the construction, MCMC algorithms are used to quantify the uncertainty in parameters due to measurement errors. We then combine uncertainties from the input parameters and measurement errors, and construct prediction intervals for the quantity of interest by propagating uncertainties through the models.
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Smith, Curtis L., Dana L. Kelly, and Kurt G. Vedros. "A Modern Approach to Bayesian Inference for Risk and Reliability Analysis." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-11831.

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Recent years have seen significant advances in the use of risk analysis in a variety of applications. Because risk and reliability models are intended to support these applications, it is critical that inference methods used in these models be robust and technically sound. The inference method described in this paper is that of Bayesian Inference. The inference we describe uses a modern computational approach known as Markov chain Monte Carlo (MCMC). MCMC methods work for simple cases, but more importantly, they work efficiently on very complex cases. Recently, with the advance of computing po
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Hosseini, Morteza, Rashidul Islam, Amey Kulkarni, and Tinoosh Mohsenin. "A Scalable FPGA-Based Accelerator for High-Throughput MCMC Algorithms." In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2017. http://dx.doi.org/10.1109/fccm.2017.56.

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Efthymiou, Charilaos. "MCMC sampling colourings and independent sets of G(n, d/n) near uniqueness threshold." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.22.

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Kumar, Ashok, Suresh Chandrasekaran, A. Chockalingam, and B. Sundar Rajan. "Near-Optimal Large-MIMO Detection Using Randomized MCMC and Randomized Search Algorithms." In ICC 2011 - 2011 IEEE International Conference on Communications. IEEE, 2011. http://dx.doi.org/10.1109/icc.2011.5963229.

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