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 9 of 9 for “"Reversible-Jump MCMC"”.
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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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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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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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Applications of Markov Chain Monte Carlo methods to continuous gravitational wave data analysis
… on strain of 7.3E-23. A further application of MCMC methods is made in the area of data analysis for the proposed LISA mission. An algorithm is developed to simultaneously estimate the number of sources and their parameters in a noisy data stream using reversible jump MCMC. An extension is made …
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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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Monte Carlo Methods in Practice and Efficiency Enhancements via Parallel Computation
… 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 efficient algorithms, and significant attention in the literature is devoted to developing algorithms that …
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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 …