Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"Reversible Jump Markov Chain Monte Carlo"”.
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Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo
This thesis consists ideas of two new population Markov chain Monte Carlo algorithms and an automatic proposal mechanism for the Reversible jump Markov chain Monte Carlo algorithm.
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Bayesian Inference of Phylogenetic Networks
… dimensions in the space of models, we devised a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under the MSNC. Given the reticulate evolutionary histories for the whole genome, we devised a method to quantify …
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Efficient MCMC inference for remote sensing of emission sources
… from measurements taken by an aircraft. We use Reversible-jump Markov chain Monte Carlo sampling to jointly infer the posterior distribution of the number of emitters and emitter properties. Additionally, we develop performance metrics that can be efficiently computed using the sample-based …
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A Statistical Model to Determine Multiple Binding Sites of a Transcription Factor on DNA Using ChIP-seq Data
… the EM algorithm. For the Bayesian method the reversible jump Markov chain Monte Carlo (RJMCMC) method is used for computation. An extensive simulation study was performed for the selection of proposal methods and priors in RJMCMC as well as for the comparison of model selection criteria in the …
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Bayesian methods in music modelling
… Where full Bayesian inference is possible, reversible-jump Markov Chain Monte Carlo is employed to estimate the number of notes and partial frequency components in each frame of music. We also use approximate techniques such as model selection criteria and variational Bayes methods for …
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Quantifying emissions of carbon dioxide and methane in central and eastern Africa through high frequency measurements and inverse modeling
… during the northern winter. We have used the Reversible Jump Markov Chain Monte Carlo methods to estimated optimized methane and carbon dioxide emissions in the Central and East African region. We have found that the region emitted about 25 Tg of CH4 and 139 Tg of C02 in 2016.
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Computational and statistical approaches to optical spectroscopy
… uses a hierarchical Bayesian inference model and reversible-jump Markov chain Monte Carlo (RJMCMC) computation with a minimum training sample size requirement. In the last part, we numerically investigate the spectral characteristics and signal requirements for universal and predictive …
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Reconstructing spatially heterogeneous thermal maps using light-based metrology sensors
… index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty …
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New marked point process models for microscopy images
… posterior marginals (EM/MPM) method, using the Markov random field (MRF) prior model. Experiments show the proposed method improves the segmentation result at object boundaries.</p> <p>The next phase of the image segmentation is detecting image features. In the second part of this research, we …
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Novel methods for biological network inference: an application to circadian Ca2+ signaling network
… Bayesian learning (GESBL), the kernel method and reversible jump Markov chain Monte Carlo method (RJMCMC). All methods are tested with challenging dynamical network simulations (including feedback, random networks, different levels of noise and number of samples), and realistic models of circadian …
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Mapping Genes for Complex Traits: Obesity, Diabetes, Hypertension, and Dyslipidemia on the Pacific Island of Kosrae
… to be used. This has been implemented in the reversible jump Markov chain Monte Carlo method Loki, which can carry out segregation and linkage analysis on quantitative traits in large pedigrees with multipoint analysis. Loki can model the trait with covariates, identify the number of …
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Bayesian analysis of finite mixture distributions using the allocation sampler
… evaluated. Richardson and Green (1997) define a reversible jump Markov chain Monte Carlo (RJMCMC) sampler while Stephens (2000a) uses a Markov birth-death process approach sample from the posterior distribution. In this thesis a Markov chain Monte Carlo method, named the allocation sampler. This …
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Latent class profile analysis : inference, estimation and its applications
… address this problem, we propose two solutions, reversible jump MCMC and the Bayesian non-parametric approach, so as to provide a set of principles for the systematic model selection for the stage-sequential process. The reversible jump MCMC sampler can explore parameter space and automatically …
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Monte Carlo Methods in Practice and Efficiency Enhancements via Parallel Computation
Monte Carlo methods are crucial when dealing with advanced problems in Bayesian inference. Indeed, common approaches such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) can be endlessly adapted to tackle the most complex problems. What is important then is to construct …