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 20 of 26 for “"reversible jump"”.
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Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo
… and an automatic proposal mechanism for the Reversible jump Markov chain Monte Carlo algorithm.
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On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions
… and develop a highly practical implementation of reversible jump, the most generalized form of MetropolisHastings. Finally, we combine these two algorithms into reversible jump weighted particle tempering, and apply it on a model and dataset that was partially collected by the author and his …
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Topics in Bayesian sample size determination and Bayesian model selection.
… methods are Bayesian model averaging and reversible jump MCMC. It is found that reversible jump MCMC, expected to give better models, does not seem to differ from Bayesian model averaging in examples considered.
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Default Bayesian model determination for generalised liner mixed models
… two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed.<br/><br/>This strategy is applied to four examples throughout this thesis.
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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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Multivariate Nonstationary Time Series: Spectrum Analysis and Dimension Reduction
… location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. The second part of the dissertation aims to shed lights on the usefulness of contemporaneous aggregation for …
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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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Combining measurements with deterministic model outputs: predicting ground-level ozone
… other components such as ensemble models, reversible jump MCMC for variable selection. However, the BM model is purely a spatial model and we generally have to deal with space-time dataset in practice. The deficiency of the BM approach leads us to a second approach, an alternative to the BM …
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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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Applications of Markov Chain Monte Carlo methods to continuous gravitational wave data analysis
… their parameters in a noisy data stream using reversible jump MCMC. An extension is made to estimate the position in the sky of a source and this is further improved by the implementation of a fast approximate calculation of the covariance matrix to enhance acceptance rates. This new algorithm …
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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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Full waveform inversion of ultrasonic phased array data for the tomographic reconstruction of heterogeneous media for NDT
… data; and a Bayesian framework, namely the reversible jump Marko chain Monte Carlo method (rj-MCMC), to perform the tomographic reconstruction in the form of a posterior distribution. The reconstructed wave speed maps are then used in conjunction with an imaging algorithm (TFM), to provide …
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Signal separation of musical instruments: simulation-based methods for musical signal decomposition and transcription
… distribution is of variable dimension and so reversible jump MCMC simulation techniques are employed for the parameter estimation task. The extension of the model to time-varying signals with high posterior correlations between model parameters is described. The parameters and hyperparameters …
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Hidden states, hidden structures: Bayesian learning in time series models
… system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case …
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Large-scale probabilistic aerial reconstruction
… By coupling edge transformations within a reversible-jump MCMC framework, we allow changes in the number of triangles and mesh connectivity. We demonstrate that these data-driven updates lead to more accurate representations while reducing modeling assumptions and utilizing fewer triangles. …
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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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Modeling spatial patterns of mixed-species Appalachian forests with Gibbs point processes
… Monte Carlo (MCMC) methods; in particular, a reversible-jump Metropolis-Hastings algorithm with birth, death, and shift proposals was utilized. Parameters for the models were estimated by a Bayesian inferential procedure that utilizes MCMC methods to draw samples from the Gibbs posterior …
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