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 40 for “"MCMC Methods"”.
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Water quality prediction for river basin management
Water quality prediction methods are developed which provide realistic estimates of prediction errors and accordingly increase the efficiency of river basin management and the implementation of EU's Water Framework Directive. The resulting river basin management decisions are based on realistic …
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Slice Sampling with Multivariate Steps
Markov chain Monte Carlo (MCMC) allows statisticians to sample from a wide variety of multidimensional probability distributions. Unfortunately, MCMC is often difficult to use when components of the target distribution are highly correlated or have disparate variances. This thesis presents three …
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Humpback whales, rock lobsters and mathematics : exploration of assessment models incorporating stock-structure
… (SIR) as well as the Markov Chain Monte Carlo (MCMC) methods.
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Modeling spatial patterns of mixed-species Appalachian forests with Gibbs point processes
… were simulated using Markov Chain 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 …
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Bayesian Clustering Approaches for Discrete Data
… of mixture models and Markov chain Monte Carlo (MCMC) methods in clustering of discrete data from high-throughput transcriptome sequencing technologies is presented. After outlining current challenges and gaps in research with respect to clustering approaches, three mixture model-based clustering …
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Variational Approximation for Complex Regression Models
… and developing fast variational approximation methods for fitting them under a Bayesian framework. Models considered include mixtures of heteroscedastic regression models, mixtures of linear mixed models and generalized linear mixed models. The advantages of variational methods as compared to …
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Bayesian Estimation of Material Properties in Case of Correlated and Insufficient Data
… Bayesian approaches as Markov-chain Monte Carlo (MCMC) methods demonstrated to be reliable and suitable tools to process data, describing probability distributions and uncertainty bounds for investigated parameters in absence of explicit inverse analytical expressions. Though it is necessary to …
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Factor graphs and MCMC approaches to iterative equalization of nonlinear dispersive channels
… strategy is based on Markov chain Monte Carlo (MCMC) methods. We typecast the problem of executing forward-backward algorithm on HMM into an MCMC domain problem and develop four different types of MCMC equalizers.
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Towards Better Representations with Deep/Bayesian Learning
… Bayesian deep learning, scalable Bayesian methods are proposed to learn the weight uncertainty of deep neural networks (DNNs). On this topic, I propose the preconditioned stochastic gradient MCMC methods, then show its connection to Dropout, and its applications to modern network …
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Bayesian sample-size determination and adaptive design for clinical trials with Poisson outcomes.
… most experimenters have employed frequentist methods, the Bayesian paradigm offers a wide variety of methodologies and are becoming increasingly more popular in clinical trials because of their flexibility and their ease of interpretation. Recently, Bayesian approaches have been used to …
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Bayesian analysis of spatial and survival models with applications of computation techniques
… spatial effects. Markov chain Monte Carlo (MCMC) methods are used in the sampling. The Ancillary-Sufficient Interweaving Strategy (ASIS) is applied to improve the performance for some parameters. The convergence of some of the parameters improved greatly, but the others do not have very …
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Hydrologic Process Parameterization of Electrical Resistivity Imaging of Solute Plumes Using POD MCMC
Markov chain Monte Carlo (MCMC) techniques have attracted wide attention in geophysical estimation of hydrogeological properties due to their ability to recover multiple, equally probable solutions that enable uncertainty assessment. Standard MCMC methods, however, become computationally …
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On the Effect of Ignoring Within-Unit Infectious Disease Dynamics When Modelling Spatial Transmission
… epidemic data through Markov chain Monte Carlo (MCMC) methods in a Bayesian statistical framework. Here, we test the effect of ignoring within-unit (e.g., city) infectious disease dynamics when we model spatial transmission. We do this by generating our epidemic data sets from a true model which …
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Linkage Based Dirichlet Processes
… developing feasible Markov Chain Monte Carlo (MCMC) algorithms for large parameter spaces. Dirichlet Process Mixture Models (DPMMs) have become a Bayesian mainstay for modeling heterogeneous structures, namely clusters, especially when the quantity of clusters is not known with the established …
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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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Sampling architectures for probabilistic inference
… Our work focuses on inference via sampling methods, in particular, Markov chain Monte Carlo (MCMC) methods. Roughly speaking, we generate samples from the distribution of labels implied by the structure of the graphical model, and use results computed from the samples to approximate the …
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Three Papers on the Political Consequences of Oil Prices
… change-point model and implementing it using MCMC methods, I find that fuel price shifts are related to increased trade networks, especially for oil-exporting countries.</p>
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Hidden states, hidden structures: Bayesian learning in time series models
This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio …
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Monte Carlo Methods for Motion Planning and Goal Inference
… the former, we utilize Markov Chain Monte Carlo (MCMC) methods to obtain trajectory samples that approximate the Boltzmann distribution, a common model for approximate rationality, which incorporates a cost function derived from trajectory optimization literature. For the latter, we develop a …
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Optimizing Irreversible Perturbations of the Unadjusted Langevin Algorithm
… normalizing constant. Markov chain Monte Carlo (MCMC) methods, and in particular Langevin dynamics, provide a powerful framework for this task by constructing stochastic processes that converge to the target distribution. However, practical implementations face two challenges: slow mixing when …
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