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Relevant bibliographies by topics / Random walk Metropolis-Hastings algorithms

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Academic literature on the topic 'Random walk Metropolis-Hastings algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Random walk Metropolis-Hastings algorithms"

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Masuhr, Andreas, and Mark Trede. "Bayesian estimation of generalized partition of unity copulas." Dependence Modeling 8, no. 1 (2020): 119–31. http://dx.doi.org/10.1515/demo-2020-0007.

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AbstractThis paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18]. The first approach is a random walk Metropolis-Hastings (RW-MH) algorithm, the second one is a random blocking random walk Metropolis-Hastings algorithm (RBRW-MH). Both approaches are Markov chain Monte Carlo methods and can cope with ˛at priors. We carry out simulation studies to determine and compare the efficiency of the algorithms. We present an empirical illustration where GPUCs are used to nonparametrically de

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Kamatani, K. "Ergodicity of Markov chain Monte Carlo with reversible proposal." Journal of Applied Probability 54, no. 2 (2017): 638–54. http://dx.doi.org/10.1017/jpr.2017.22.

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Abstract We describe the ergodic properties of some Metropolis–Hastings algorithms for heavy-tailed target distributions. The results of these algorithms are usually analyzed under a subgeometric ergodic framework, but we prove that the mixed preconditioned Crank–Nicolson (MpCN) algorithm has geometric ergodicity even for heavy-tailed target distributions. This useful property comes from the fact that, under a suitable transformation, the MpCN algorithm becomes a random-walk Metropolis algorithm.

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Saraiva, Erlandson, Adriano Suzuki, and Luis Milan. "Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data." Entropy 20, no. 9 (2018): 642. http://dx.doi.org/10.3390/e20090642.

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In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali–Mikhail–Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis–Hastings algorithm: Independent Metropolis–Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis–Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be diffic

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Müller, Christian, Fabian Weysser, Thomas Mrziglod, and Andreas Schuppert. "Markov-Chain Monte-Carlo methods and non-identifiabilities." Monte Carlo Methods and Applications 24, no. 3 (2018): 203–14. http://dx.doi.org/10.1515/mcma-2018-0018.

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Abstract We consider the problem of sampling from high-dimensional likelihood functions with large amounts of non-identifiabilities via Markov-Chain Monte-Carlo algorithms. Non-identifiabilities are problematic for commonly used proposal densities, leading to a low effective sample size. To address this problem, we introduce a regularization method using an artificial prior, which restricts non-identifiable parts of the likelihood function. This enables us to sample the posterior using common MCMC methods more efficiently. We demonstrate this with three MCMC methods on a likelihood based on a

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Müller, Christian, Holger Diedam, Thomas Mrziglod, and Andreas Schuppert. "A neural network assisted Metropolis adjusted Langevin algorithm." Monte Carlo Methods and Applications 26, no. 2 (2020): 93–111. http://dx.doi.org/10.1515/mcma-2020-2060.

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AbstractIn this paper, we derive a Markov chain Monte Carlo (MCMC) algorithm supported by a neural network. In particular, we use the neural network to substitute derivative calculations made during a Metropolis adjusted Langevin algorithm (MALA) step with inexpensive neural network evaluations. Using a complex, high-dimensional blood coagulation model and a set of measurements, we define a likelihood function on which we evaluate the new MCMC algorithm. The blood coagulation model is a dynamic model, where derivative calculations are expensive and hence limit the efficiency of derivative-base

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Livingstone, Samuel. "Geometric Ergodicity of the Random Walk Metropolis with Position-Dependent Proposal Covariance." Mathematics 9, no. 4 (2021): 341. http://dx.doi.org/10.3390/math9040341.

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We consider a Metropolis–Hastings method with proposal N(x,hG(x)−1), where x is the current state, and study its ergodicity properties. We show that suitable choices of G(x) can change these ergodicity properties compared to the Random Walk Metropolis case N(x,hΣ), either for better or worse. We find that if the proposal variance is allowed to grow unboundedly in the tails of the distribution then geometric ergodicity can be established when the target distribution for the algorithm has tails that are heavier than exponential, in contrast to the Random Walk Metropolis case, but that the growth

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Kawahara, Kazuaki, Ryo Ishikawa, Takuma Higashi, et al. "Unique fitting of electrochemical impedance spectra by random walk Metropolis Hastings algorithm." Journal of Power Sources 403 (November 2018): 184–91. http://dx.doi.org/10.1016/j.jpowsour.2018.09.091.

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Vištica, Marija, Ani Grubišic, and Branko Žitko. "Applying graph sampling methods on student model initialization in intelligent tutoring systems." International Journal of Information and Learning Technology 33, no. 4 (2016): 202–18. http://dx.doi.org/10.1108/ijilt-03-2016-0011.

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Purpose – In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue. Design/methodology/approach – In order to generate a knowledge sample that represents truly a certain domain knowledge, the authors can use sampling algorithms. In this paper, the authors present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball an

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Bédard, Mylène. "Hierarchical Models and Tuning of Random Walk Metropolis Algorithms." Journal of Probability and Statistics 2019 (August 26, 2019): 1–24. http://dx.doi.org/10.1155/2019/8740426.

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We obtain weak convergence and optimal scaling results for the random walk Metropolis algorithm with a Gaussian proposal distribution. The sampler is applied to hierarchical target distributions, which form the building block of many Bayesian analyses. The global asymptotically optimal proposal variance derived may be computed as a function of the specific target distribution considered. We also introduce the concept of locally optimal tunings, i.e., tunings that depend on the current position of the Markov chain. The theorems are proved by studying the generator of the first and second compon

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Vihola, Matti. "Grapham: Graphical models with adaptive random walk Metropolis algorithms." Computational Statistics & Data Analysis 54, no. 1 (2010): 49–54. http://dx.doi.org/10.1016/j.csda.2009.09.001.

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Dissertations / Theses on the topic "Random walk Metropolis-Hastings algorithms"

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Kosowski, Adrian. "Time and Space-Efficient Algorithms for Mobile Agents in an Anonymous Network." Habilitation à diriger des recherches, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00867765.

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Computing with mobile agents is rapidly becoming a topic of mainstream research in the theory of distributed computing. The main research questions undertaken in this study concern the feasibility of solving fundamental tasks in an anonymous network, subject to limitations on the resources available to the agent. The considered challenges include: exploring a graph by means of an agent with limited memory, discovery of the network topology, and attempting to meet with another agent in another network (rendezvous). The constraints imposed on the agent include the number of moves which the agent

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Mendis, Ruchini Dilinika. "Sensitivity Analyses for Tumor Growth Models." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3113.

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This study consists of the sensitivity analysis for two previously developed tumor growth models: Gompertz model and quotient model. The two models are considered in both continuous and discrete time. In continuous time, model parameters are estimated using least-square method, while in discrete time, the partial-sum method is used. Moreover, frequentist and Bayesian methods are used to construct confidence intervals and credible intervals for the model parameters. We apply the Markov Chain Monte Carlo (MCMC) techniques with the Random Walk Metropolis algorithm with Non-informative Prior and t

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Mireuta, Matei. "Étude de la performance d’un algorithme Metropolis-Hastings avec ajustement directionnel." Thèse, 2011. http://hdl.handle.net/1866/6231.

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Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont des outils très populaires pour l’échantillonnage de lois de probabilité complexes et/ou en grandes dimensions. Étant donné leur facilité d’application, ces méthodes sont largement répandues dans plusieurs communautés scientifiques et bien certainement en statistique, particulièrement en analyse bayésienne. Depuis l’apparition de la première méthode MCMC en 1953, le nombre de ces algorithmes a considérablement augmenté et ce sujet continue d’être une aire de recherche active. Un nouvel algorithme MCMC avec ajustement directionnel a é

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Groiez, Assia. "Recyclage des candidats dans l'algorithme Metropolis à essais multiples." Thèse, 2014. http://hdl.handle.net/1866/10853.

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Les méthodes de Monte Carlo par chaînes de Markov (MCCM) sont des méthodes servant à échantillonner à partir de distributions de probabilité. Ces techniques se basent sur le parcours de chaînes de Markov ayant pour lois stationnaires les distributions à échantillonner. Étant donné leur facilité d’application, elles constituent une des approches les plus utilisées dans la communauté statistique, et tout particulièrement en analyse bayésienne. Ce sont des outils très populaires pour l’échantillonnage de lois de probabilité complexes et/ou en grandes dimensions. Depuis l’apparition de la première

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Reddy, Chandan Rama. "Capacity Proportional Unstructured Peer-to-Peer Networks." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-878.

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Existing methods to utilize capacity-heterogeneity in a P2P system either rely on constructing special overlays with capacity-proportional node degree or use topology adaptation to match a node's capacity with that of its neighbors. In existing P2P networks, which are often characterized by diverse node capacities and high churn, these methods may require large node degree or continuous topology adaptation, potentially making them infeasible due to their high overhead. In this thesis, we propose an unstructured P2P system that attempts to address these issues. We first prove that the overall t

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Lalancette, Michaël. "Convergence d’un algorithme de type Metropolis pour une distribution cible bimodale." Thèse, 2017. http://hdl.handle.net/1866/19376.

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Nous présentons dans ce mémoire un nouvel algorithme de type Metropolis-Hastings dans lequel la distribution instrumentale a été conçue pour l'estimation de distributions cibles bimodales. En fait, cet algorithme peut être vu comme une modification de l'algorithme Metropolis de type marche aléatoire habituel auquel on ajoute quelques incréments de grande envergure à des moments aléatoires à travers la simulation. Le but de ces grands incréments est de quitter le mode de la distribution cible où l'on se trouve et de trouver l'autre mode. Par la suite, nous présentons puis démontrons un résul

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Gagnon, Philippe. "Sélection de modèles robuste : régression linéaire et algorithme à sauts réversibles." Thèse, 2017. http://hdl.handle.net/1866/20583.

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Books on the topic "Random walk Metropolis-Hastings algorithms"

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Bedard, Mylene. On the robustness of optimal scaling for random walk Metropolis algorithms. 2006.

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Bi, Xiaojun, Brian Smith, Tom Ouyang, and Shumin Zhai. Soft Keyboard Performance Optimization. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0006.

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Optimization techniques have played a vital role in improving the performance (i.e., input speed and accuracy) of soft keyboards. This chapter introduces the challenges, methodologies, and results of keyboard performance optimization. Leveraging the robust human motor control phenomena manifested in text entry, we used the Metropolis random walk algorithm, and Pareto multi-objective optimization method to optimize the keyboard layout and a soft keyboard decoder. The optimization led to layouts that shorten finger travel distance and improve the input speed as well as accuracy over the Qwerty l

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Book chapters on the topic "Random walk Metropolis-Hastings algorithms"

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Herbst, Edward P., and Frank Schorfheide. "Metropolis-Hastings Algorithms for DSGE Models." In Bayesian Estimation of DSGE Models. Princeton University Press, 2015. http://dx.doi.org/10.23943/princeton/9780691161082.003.0004.

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This chapter talks about the most widely used method to generate draws from posterior distributions of a DSGE model: the random walk MH (RWMH) algorithm. The DSGE model likelihood function in combination with the prior distribution leads to a posterior distribution that has a fairly regular elliptical shape. In turn, the draws from a simple RWMH algorithm can be used to obtain an accurate numerical approximation of posterior moments. However, in many other applications, particularly those involving medium- and large-scale DSGE models, the posterior distributions could be very non-elliptical. Irregularly shaped posterior distributions are often caused by identification problems or misspecification. In lieu of the difficulties caused by irregularly shaped posterior surfaces, the chapter reviews various alternative MH samplers, which use alternative proposal distributions.

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Donovan, Therese M., and Ruth M. Mickey. "The White House Problem Revisited: MCMC with the Metropolis–Hastings Algorithm." In Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.003.0015.

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The “White House Problem” of Chapter 10 is revisited in this chapter. Markov Chain Monte Carlo (MCMC) is used to build the posterior distribution of the unknown parameter p, the probability that a famous person could gain access to the White House without invitation. The chapter highlights the Metropolis–Hastings algorithm in MCMC analysis, describing the process step by step. The posterior distribution generated in Chapter 10 using the beta-binomial conjugate is compared with the MCMC posterior distribution to show how successful the MCMC method can be. By the end of this chapter, the reader will have a firm understanding of the following concepts: Monte Carlo, Markov chain, Metropolis–Hastings algorithm, Metropolis–Hastings random walk, and Metropolis–Hastings independence sampler.

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Conference papers on the topic "Random walk Metropolis-Hastings algorithms"

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Lee, Chul-Ho, Xin Xu, and Do Young Eun. "Beyond random walk and metropolis-hastings samplers." In the 12th ACM SIGMETRICS/PERFORMANCE joint international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2254756.2254795.

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Matsumura, Toshiki, and Kazuyuki Shudo. "Metropolis-Hastings Random Walk with a Reduced Number of Self-Loops." In 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2019. http://dx.doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00229.

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Gowda, Niranjan M., Sundar Krishnamurthy, and Andrey Belogolovy. "Metropolis-Hastings Random Walk along the Gradient Descent Direction for MIMO Detection." In ICC 2021 - IEEE International Conference on Communications. IEEE, 2021. http://dx.doi.org/10.1109/icc42927.2021.9500309.

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Cem, Emrah, and Kamil Sarac. "Estimating clustering coefficients via metropolis-hastings random walk and wedge sampling on large OSN graphs." In 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC). IEEE, 2016. http://dx.doi.org/10.1109/pccc.2016.7820606.

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Pina-Garcia, C. A., and Dongbing Gu. "Collecting Random Samples from Facebook: An Efficient Heuristic for Sampling Large and Undirected Graphs via a Metropolis-Hastings Random Walk." In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.384.

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Reports on the topic "Random walk Metropolis-Hastings algorithms"

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Graves, Todd L. Automatic step size selection in randon walk Metropolis algorithms. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1057119.

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