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 24 for “"mcmc sampling"”.
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Bayesian phylogenetic models for relaxed clock and trait evolution
Bayesian Markov chain Monte Carlo (MCMC) has become a common approach for phylogenetic inference. While huge amount of data provides signi cant information of evolution, phylogenetic inference of larger data sets requires more e cient MCMC methods. In the meantime, it remains challenging to …
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Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
… updating equation''. Markov Chain Monte Carlo (MCMC) sampling is applied to explore the posterior distributions and generate samples from which we can extract statistical information for the uncertain input parameters. To greatly alleviate the computational burden during MCMC sampling, we used …
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Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data
… package performs Markov Chain Monte Carlo (MCMC) sampling and outputs posterior medians, intervals, and trace plots for fixed effect and spatial parameters. Finally, the functions provide regional summaries, including medians and credible intervals for fitted values by subregion.
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Bayesian generalized additive model selection
… enable the Markov chain Monte Carlo (MCMC) sampling reduce to the Gibbs sampling or to slice sampling. To improve computational scalability and speed, we also derive the mean field variational Bayes (MFVB) algorithms under the Laplace-Zero and Grouped Lasso-Zero priors. The GAM …
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Quantifying uncertainty in computational neuroscience with Bayesian statistical inference
… In the first, Markov chain Monte Carlo (MCMC) sampling methods were used to answer a range of scientific questions that arise in the analysis of physiological data from tuning curve experiments; in addition, a software toolbox is described that makes these methods widely accessible. In …
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Floor Plan Design Collaborator: A Data-Driven Approach to Assist Human Architects in Design Exploration
… examples through a Markov Chain Monte Carlo (MCMC) sampling procedure. Experiments on real-world examples demonstrate that the framework effectively summarizes the statistical information of given design examples and generates unseen examples based on the learned knowledge. The transparency of …
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Modeling Time-Varying Networks with Applications to Neural Flow and Genetic Regulation
… from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily generalizes under varying levels of uncertainty about the data generation process. We then characterize the nature of evolving networks in several biological datasets.</p><p>We initially focus on learning how …
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Efficient MCMC inference for material detection and classification In tomography
… of Probabilistic Inference to implement MCMC sampling of realizations of the latent configuration conditioned on the measurements. We exploit conditional-independence properties of the graphical-model representation to sample many nodes in parallel and thereby render our sampling scheme …
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Parameter and state model reduction for Bayesian statistical inverse problems
… space, we must sample. Markov chain Monte Carlo (MCMC) sampling provides a method by which a random walk is constructed through parameter space. By following a few simple rules, the random walk converges to the posterior distribution and the resulting samples represent draws from that …
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Missing data imputation in a clinical registry with deep generative models
… a robust and efficient Markov Chain Monte Carlo (MCMC) sampling technique to estimate probability density of a given point. Two different Markov Chains, the random walk Metropolis and Hamiltonian Markov Chain were compared by their convergence speed. For imputation, we conducted synthetic …
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Bayesian spatio-temporal modelling for forecasting ground level ozone concentration levels
… requiring the use of Markov chain Monte Carlo (MCMC) techniques, is developed for forecasting daily ozone concentration levels. A set of model validation analyses shows that the prediction maps that are generated by the aforementioned models are more accurate than the maps based solely on the …
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Measurement Error Adjustment in the Offset Variable of a Poisson Model
… in a Bayesian framework, and the Bayesian MCMC sampling is implemented in rstan. To check whether the adjustment for the offset variable will bring any differences to our model, we have conducted real data analysis. We found the coefficient of T (time) becomes less significant after the …
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Bayesian modelling and sampling strategies for ordering and clustering problems with a focus on next-generation sequencing data
… two parts focus on the development of models and sampling strategies specifically tailored for next-generation sequencing data. Most high-throughput measurements for single-cell data are destructive, resulting in the loss of longitudinal information. I developed a new, Bayesian, way of …
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New applications of statistics in astronomy and cosmology
… marginalization and Markov Chain Monte Carlo (MCMC) sampling over the unknown type of the supernova, showing that it recovers unbiased cosmological parameters with good coverage. We then apply Bayesian statistics to the field of radio interferometry. This is particularly relevant in light of …
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Sampling in computer vision and Bayesian nonparametric mixtures
… how efficient Markov chain Monte Carlo (MCMC) sampling techniques can address a subset of these problems. In the first half of this thesis, we consider the problem of inference in discrete Markov random fields (MRFs) that often occur in segmentation and tracking. We develop the …
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Learning Structures : fusing deconvolution-based seismic interferometry with Bayesian inference for structural health assessment
… We employ a sequential Markov Chain Monte Carlo (MCMC) sampling to obtain a baseline structural model. Through the comparison of the model parameter distributions with the baseline information, we show that the damage localization and quantification is possible. We initially test our procedure …
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GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY
The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number …
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Bayesian Balance Regression And Mediation Analysis For Microbiome Compositional Data
… total count is not informative because of the sampling, sample preparation and sequencing processes. These counts are used to obtain estimates of the relative abundance of the taxa, which is com- positional with a unit sum constraint. Analysis of compositional data requires special statistical …
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A Comparison of Bayesian Regression Models Applied in Knot Theory
This thesis explores variations on a Bayesian regression model used to estimate the mean box length of a random knot as a function of the number of edges of that knot. Specifically, this research recognizes uncertainty in box length variance and compares the resulting inference with that based on …
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Statistical Methods for Multi-type Recurrent Event Data Based on Monte Carlo EM Algorithms and Copula Frailties
… random effects. An MCEM algorithm with MCMC routines in the E-step is adopted for the partial likelihood to estimate model parameters. Equations for the variances of the estimates are derived and variances of estimates are computed by Louis' formula. Predictions of the individual random …
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