Дисертації з теми "Markov chain Monte Carlo samplers"
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Guha, Subharup. "Benchmark estimation for Markov Chain Monte Carlo samplers." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085594208.
Повний текст джерелаSisson, Scott Antony. "Markov chains for genetics and extremes." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391095.
Повний текст джерелаPang, Wan-Kai. "Modelling ordinal categorical data : a Gibbs sampler approach." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323876.
Повний текст джерелаVerhelst, Norman D., Reinhold Hatzinger, and Patrick Mair. "The Rasch Sampler." Foundation for Open Access Statistics, 2007. http://dx.doi.org/10.18637/jss.v020.i04.
Повний текст джерелаZhu, Qingyun. "Product Deletion and Supply Chain Management." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/527.
Повний текст джерелаAl, Hakmani Rahab. "Bayesian Estimation of Mixture IRT Models using NUTS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1641.
Повний текст джерелаLu, Pingbo. "Calibrated Bayes factors for model selection and model averaging." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343396705.
Повний текст джерелаDeng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center
Wu, Yi-Fang. "Accuracy and variability of item parameter estimates from marginal maximum a posteriori estimation and Bayesian inference via Gibbs samplers." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/5879.
Повний текст джерелаFu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.
Повний текст джерелаFrühwirth-Schnatter, Sylvia. "Bayesian Model Discrimination and Bayes Factors for Normal Linear State Space Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1993. http://epub.wu.ac.at/108/1/document.pdf.
Повний текст джерелаSeries: Forschungsberichte / Institut für Statistik
Bakra, Eleni. "Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1247/.
Повний текст джерелаHelali, Amine. "Vitesse de convergence de l'échantillonneur de Gibbs appliqué à des modèles de la physique statistique." Thesis, Brest, 2019. http://www.theses.fr/2019BRES0002/document.
Повний текст джерелаMonte Carlo Markov chain methods MCMC are mathematical tools used to simulate probability measures π defined on state spaces of high dimensions. The speed of convergence of this Markov chain X to its invariant state π is a natural question to study in this context.To measure the convergence rate of a Markov chain we use the total variation distance. It is well known that the convergence rate of a reversible Markov chain depends on its second largest eigenvalue in absolute value denoted by β!. An important part in the estimation of β! is the estimation of the second largest eigenvalue which is denoted by β1.Diaconis and Stroock (1991) introduced a method based on Poincaré inequality to obtain a bound for β1 for general finite state reversible Markov chains.In this thesis we use the Chen and Shiu approach to study the case of the Gibbs sampler for the 1−D Ising model with three and more states which is also called Potts model. Then, we generalize the result of Shiu and Chen (2015) to the case of the 2−D Ising model with two states.The results we obtain improve the ones obtained by Ingrassia (1994). Then, we introduce some method to disrupt the Gibbs sampler in order to improve its convergence rate to equilibrium
Holenstein, Roman. "Particle Markov chain Monte Carlo." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7319.
Повний текст джерелаByrd, Jonathan Michael Robert. "Parallel Markov Chain Monte Carlo." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3634/.
Повний текст джерелаHörmann, Wolfgang, and Josef Leydold. "Improved Perfect Slice Sampling." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/868/1/document.pdf.
Повний текст джерелаSeries: Preprint Series / Department of Applied Statistics and Data Processing
Zhang, Yichuan. "Scalable geometric Markov chain Monte Carlo." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20978.
Повний текст джерелаFang, Youhan. "Efficient Markov Chain Monte Carlo Methods." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10809188.
Повний текст джерелаGenerating random samples from a prescribed distribution is one of the most important and challenging problems in machine learning, Bayesian statistics, and the simulation of materials. Markov Chain Monte Carlo (MCMC) methods are usually the required tool for this task, if the desired distribution is known only up to a multiplicative constant. Samples produced by an MCMC method are real values in N-dimensional space, called the configuration space. The distribution of such samples converges to the target distribution in the limit. However, existing MCMC methods still face many challenges that are not well resolved. Difficulties for sampling by using MCMC methods include, but not exclusively, dealing with high dimensional and multimodal problems, high computation cost due to extremely large datasets in Bayesian machine learning models, and lack of reliable indicators for detecting convergence and measuring the accuracy of sampling. This dissertation focuses on new theory and methodology for efficient MCMC methods that aim to overcome the aforementioned difficulties.
One contribution of this dissertation is generalizations of hybrid Monte Carlo (HMC). An HMC method combines a discretized dynamical system in an extended space, called the state space, and an acceptance test based on the Metropolis criterion. The discretized dynamical system used in HMC is volume preserving—meaning that in the state space, the absolute Jacobian of a map from one point on the trajectory to another is 1. Volume preservation is, however, not necessary for the general purpose of sampling. A general theory allowing the use of non-volume preserving dynamics for proposing MCMC moves is proposed. Examples including isokinetic dynamics and variable mass Hamiltonian dynamics with an explicit integrator, are all designed with fewer restrictions based on the general theory. Experiments show improvement in efficiency for sampling high dimensional multimodal problems. A second contribution is stochastic gradient samplers with reduced bias. An in-depth analysis of the noise introduced by the stochastic gradient is provided. Two methods to reduce the bias in the distribution of samples are proposed. One is to correct the dynamics by using an estimated noise based on subsampled data, and the other is to introduce additional variables and corresponding dynamics to adaptively reduce the bias. Extensive experiments show that both methods outperform existing methods. A third contribution is quasi-reliable estimates of effective sample size. Proposed is a more reliable indicator—the longest integrated autocorrelation time over all functions in the state space—for detecting the convergence and measuring the accuracy of MCMC methods. The superiority of the new indicator is supported by experiments on both synthetic and real problems.
Minor contributions include a general framework of changing variables, and a numerical integrator for the Hamiltonian dynamics with fourth order accuracy. The idea of changing variables is to transform the potential energy function as a function of the original variable to a function of the new variable, such that undesired properties can be removed. Two examples are provided and preliminary experimental results are obtained for supporting this idea. The fourth order integrator is constructed by combining the idea of the simplified Takahashi-Imada method and a two-stage Hessian-based integrator. The proposed method, called two-stage simplified Takahashi-Imada method, shows outstanding performance over existing methods in high-dimensional sampling problems.
Neuhoff, Daniel. "Reversible Jump Markov Chain Monte Carlo." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17461.
Повний текст джерелаThe four studies of this thesis are concerned predominantly with the dynamics of macroeconomic time series, both in the context of a simple DSGE model, as well as from a pure time series modeling perspective.
Andersson, Lovisa. "An application of Bayesian Hidden Markov Models to explore traffic flow conditions in an urban area." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385187.
Повний текст джерелаLuo, Yuqun. "Incorporation of Genetic Marker Information in Estimating Modelparameters for Complex Traits with Data From Large Complex Pedigrees." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039109696.
Повний текст джерелаMurray, Iain Andrew. "Advances in Markov chain Monte Carlo methods." Thesis, University College London (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487199.
Повний текст джерелаHan, Xiao-liang. "Markov Chain Monte Carlo and sampling efficiency." Thesis, University of Bristol, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333974.
Повний текст джерелаFan, Yanan. "Efficient implementation of Markov chain Monte Carlo." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343307.
Повний текст джерелаBrooks, Stephen Peter. "Convergence diagnostics for Markov Chain Monte Carlo." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363913.
Повний текст джерелаGraham, Matthew McKenzie. "Auxiliary variable Markov chain Monte Carlo methods." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28962.
Повний текст джерелаStormark, Kristian. "Multiple Proposal Strategies for Markov Chain Monte Carlo." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9330.
Повний текст джерелаThe multiple proposal methods represent a recent simulation technique for Markov Chain Monte Carlo that allows several proposals to be considered at each step of transition. Motivated by the ideas of Quasi Monte Carlo integration, we examine how strongly correlated proposals can be employed to construct Markov chains with improved mixing properties. We proceed by giving a concise introduction to the Monte Carlo and Markov Chain Monte Carlo theory, and we supply a short discussion of the standard simulation algorithms and the difficulties of efficient sampling. We then examine two multiple proposal methods suggested in the literature, and we indicate the possibility of a unified formulation of the two methods. More essentially, we report some systematic exploration strategies for the two multiple proposals methods. In particular, we present schemes for the utilization of well-distributed point sets and maximally spread search directions. We also include a simple construction procedure for the latter type of point set. A numerical examination of the multiple proposal methods are performed on two simple test problems. We find that the systematic exploration approach may provide a significant improvement of the mixing, especially when the probability mass of the target distribution is ``easy to miss'' by independent sampling. For both test problems, we find that the best results are obtained with the QMC schemes. In particular, we find that the gain is most pronounced for a relatively moderate number of proposal. With fewer proposals, the properties of the well-distributed point sets will no be that relevant. For a large number of proposals, the independent sampling approach will be more competitive, since the coverage of the local neighborhood then will be better.
Sanborn, Adam N. "Uncovering mental representations with Markov chain Monte Carlo." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278468.
Повний текст джерелаSource: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6994. Adviser: Richard M. Shiffrin. Title from dissertation home page (viewed May 21, 2008).
Suzuki, Yuya. "Rare-event Simulation with Markov Chain Monte Carlo." Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138950.
Повний текст джерелаGudmundsson, Thorbjörn. "Rare-event simulation with Markov chain Monte Carlo." Doctoral thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157522.
Повний текст джерелаQC 20141216
Hastie, David. "Towards automatic reversible jump Markov Chain Monte Carlo." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414179.
Повний текст джерелаLi, Shuying. "Phylogenetic tree construction using markov chain monte carlo /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487942182323916.
Повний текст джерелаXu, Jason Qian. "Markov Chain Monte Carlo and Non-Reversible Methods." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/244823.
Повний текст джерелаBentley, Jason Phillip. "Exact Markov chain Monte Carlo and Bayesian linear regression." Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/2534.
Повний текст джерелаPooley, James P. "Exploring phonetic category structure with Markov chain Monte Carlo." Connect to resource, 2008. http://hdl.handle.net/1811/32221.
Повний текст джерелаAngelino, Elaine Lee. "Accelerating Markov chain Monte Carlo via parallel predictive prefetching." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070022.
Повний текст джерелаEngineering and Applied Sciences
Vaičiulytė, Ingrida. "Study and application of Markov chain Monte Carlo method." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20141209_112440-55390.
Повний текст джерелаDisertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.
Pereira, Fernanda Chaves. "Bayesian Markov chain Monte Carlo methods in general insurance." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342720.
Повний текст джерелаMangoubi, Oren (Oren Rami). "Integral geometry, Hamiltonian dynamics, and Markov Chain Monte Carlo." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104583.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 97-101).
This thesis presents applications of differential geometry and graph theory to the design and analysis of Markov chain Monte Carlo (MCMC) algorithms. MCMC algorithms are used to generate samples from an arbitrary probability density [pi] in computationally demanding situations, since their mixing times need not grow exponentially with the dimension of [pi]. However, if [pi] has many modes, MCMC algorithms may still have very long mixing times. It is therefore crucial to understand and reduce MCMC mixing times, and there is currently a need for global mixing time bounds as well as algorithms that mix quickly for multi-modal densities. In the Gibbs sampling MCMC algorithm, the variance in the size of modes intersected by the algorithm's search-subspaces can grow exponentially in the dimension, greatly increasing the mixing time. We use integral geometry, together with the Hessian of r and the Chern-Gauss-Bonnet theorem, to correct these distortions and avoid this exponential increase in the mixing time. Towards this end, we prove a generalization of the classical Crofton's formula in integral geometry that can allow one to greatly reduce the variance of Crofton's formula without introducing a bias. Hamiltonian Monte Carlo (HMC) algorithms are some the most widely-used MCMC algorithms. We use the symplectic properties of Hamiltonians to prove global Cheeger-type lower bounds for the mixing times of HMC algorithms, including Riemannian Manifold HMC as well as No-U-Turn HMC, the workhorse of the popular Bayesian software package Stan. One consequence of our work is the impossibility of energy-conserving Hamiltonian Markov chains to search for far-apart sub-Gaussian modes in polynomial time. We then prove another generalization of Crofton's formula that applies to Hamiltonian trajectories, and use our generalized Crofton formula to improve the convergence speed of HMC-based integration on manifolds. We also present a generalization of the Hopf fibration acting on arbitrary- ghost-valued random variables. For [beta] = 4, the geometry of the Hopf fibration is encoded by the quaternions; we investigate the extent to which the elegant properties of this encoding are preserved when one replaces quaternions with general [beta] > 0 ghosts.
by Oren Mangoubi.
Ph. D.
Persing, Adam. "Some contributions to particle Markov chain Monte Carlo algorithms." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23277.
Повний текст джерелаTu, Zhuowen. "Image Parsing by Data-Driven Markov Chain Monte Carlo." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1038347031.
Повний текст джерелаPaul, Rajib. "Theoretical And Algorithmic Developments In Markov Chain Monte Carlo." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218184168.
Повний текст джерелаCheal, Ryan. "Markov Chain Monte Carlo methods for simulation in pedigrees." Thesis, University of Bath, 1996. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362254.
Повний текст джерелаBALDIOTI, HUGO RIBEIRO. "MARKOV CHAIN MONTE CARLO FOR NATURAL INFLOW ENERGY SCENARIOS SIMULATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36058@1.
Повний текст джерелаCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
Constituído por uma matriz eletro-energética predominantemente hídrica e território de proporções continentais, o Brasil apresenta características únicas, sendo possível realizar o aproveitamento dos fartos recursos hídricos presentes no território nacional. Aproximadamente 65 por cento da capacidade de geração de energia elétrica advém de recursos hidrelétricos enquanto 28 por cento de recursos termelétricos. Sabe-se que regimes hidrológicos de vazões naturais são de natureza estocástica e em função disso é preciso tratá-los para que se possa planejar a operação do sistema, sendo assim, o despacho hidrotérmico é de suma importância e caracterizado por sua dependência estocástica. A partir das vazões naturais é possível calcular a Energia Natural Afluente (ENA) que será utilizada diretamente no processo de simulação de séries sintéticas que, por sua vez, são utilizadas no processo de otimização, responsável pelo cálculo da política ótima visando minimizar os custos de operação do sistema. Os estudos referentes a simulação de cenários sintéticos de ENA vêm se desenvolvendo com novas propostas metodológicas ao longo dos anos. Tais desenvolvimentos muitas vezes pressupõem Gaussianidade dos dados, de forma que seja possível ajustar uma distribuição paramétrica nos mesmos. Percebeu-se que na maioria dos casos reais, no contexto do Setor Elétrico Brasileiro, os dados não podem ser tratados desta forma, uma vez que apresentam em sua densidade comportamentos de cauda relevantes e uma acentuada assimetria. É necessário para o planejamento da operação do Sistema Interligado Nacional (SIN) que a assimetria intrínseca a este comportamento seja passível de reprodução. Dessa forma, este trabalho propõe duas abordagens não paramétricas para simulação de cenários. A primeira refere-se ao processo de amostragem dos resíduos das séries de ENA, para tanto, utiliza-se a técnica Markov Chain Monte Carlo (MCMC) e o Kernel Density Estimation. A segunda metodologia proposta aplica o MCMC Interconfigurações diretamente nas séries de ENA para simulação de cenários sintéticos a partir de uma abordagem inovadora para transição entre as matrizes e períodos. Os resultados da implementação das metodologias, observados graficamente e a partir de testes estatísticos de aderência ao histórico de dados, apontam que as propostas conseguem reproduzir com uma maior acurácia as características assimétricas sem perder a capacidade de reproduzir estatísticas básicas. Destarte, pode-se afirmar que os modelos propostos são boas alternativas em relação ao modelo vigente utilizado pelo setor elétrico brasileiro.
Consisting of an electro-energetic matrix with hydro predominance and a continental proportion territory, Brazil presents unique characteristics, being able to make use of the abundant water resources in the national territory. Approximately 65 percent of the electricity generation capacity comes from hydropower while 28 percent from thermoelectric plants. It is known that hydrological regimes have a stochastic nature and it is necessary to treat them so the energy system can be planned, thus the hydrothermal dispatch is extremely important and characterized by its stochastic dependence. From the natural streamflows it is possible to calculate the Natural Inflow Energy (NIE) that will be used directly in the synthetic series simulation process, which, in turn, are used on the optimization process, responsible for optimal policy calculation in order to minimize the system operational costs. The studies concerning the simulation of synthetic scenarios of NIE have been developing with new methodological proposals over the years. Such developments often presuppose data Gaussianity, so that a parametric distribution can be fitted to them. It was noticed that in the majority of real cases, in the context of the Brazilian Electrical Sector, the data cannot be treated like that, since they present in their density relevant tail behavior and skewness. It is necessary for the National Interconnected System (SIN) operational planning that the intrinsic skewness behavior is amenable to reproduction. Thus, this paper proposes two non-parametric approaches to scenarios simulation. The first one refers to the process of NIE series residues sampling, using a Markov Chain Monte Carlo (MCMC) technique and the Kernel Density Estimation. The second methodology is also proposed where the MCMC is applied periodically and directly in the NIE series to simulate synthetic scenarios using an innovative approach for transitions between matrices. The methodologies implementation results, observed graphically and based on statistical tests of adherence to the historical data, indicate that the proposals can reproduce with greater accuracy the asymmetric characteristics without losing the ability to reproduce basic statistics. Thus, one can conclude that the proposed models are good alternatives in relation to the current model of the Brazilian Electric Sector.
Wu, Miaodan. "Markov chain Monte Carlo methods applied to Bayesian data analysis." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625087.
Повний текст джерелаRao, V. A. P. "Markov chain Monte Carlo for continuous-time discrete-state systems." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1349490/.
Повний текст джерелаHigdon, David. "Spatial applications of Markov chain Monte Carlo for Bayesian inference /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8942.
Повний текст джерелаWu, Chang-Ye. "Acceleration Strategies of Markov Chain Monte Carlo for Bayesian Computation." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED019/document.
Повний текст джерелаMCMC algorithms are difficult to scale, since they need to sweep over the whole data set at each iteration, which prohibits their applications in big data settings. Roughly speaking, all scalable MCMC algorithms can be divided into two categories: divide-and-conquer methods and subsampling methods. The aim of this project is to reduce the computing time induced by complex or largelikelihood functions
Karawatzki, Roman, and Josef Leydold. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/294/1/document.pdf.
Повний текст джерелаSeries: Preprint Series / Department of Applied Statistics and Data Processing
Cui, Tiangang. "Bayesian calibration of geothermal reservoir models via Markov Chain Monte Carlo." Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/5944.
Повний текст джерелаWhole document restricted until September 2011, but available by request, use the feedback form to request access.