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 125 for “"Markov Chain Monte Carlo (MCMC)"”.
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Humpback whales, rock lobsters and mathematics : exploration of assessment models incorporating stock-structure
… re-sampling (SIR) as well as the Markov Chain Monte Carlo (MCMC) methods.
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Three Statistical Problems With Imprecisely or Incompletely Observed Data
… to a more accurate one. Second, we use the Markov Chain Monte Carlo (MCMC) method to handle a grouped independent variable in a linear model as motivated by a residential energy study. The third study is concerned with an approximate minimum Hellinger distance estimator (AMHDE) under …
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Bayesian analysis of multivariate stochastic volatility and dynamic models
… regression and volatility equations. We develop Markov Chain Monte Carlo (MCMC) algorithms that generate a posteriori restrictions on the elements of both the regression coefficients and the covariance matrix of the error term. Efficient parametrization of the time varying covariance matrices is …
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A Multi-GPU Compute Solution for Optimized Genomic Selection Analysis
… while others do not. One such algorithm is Markov Chain Monte Carlo (MCMC). In this thesis, we present a heterogeneous compute solution for optimizing GenSel, a genetic selection analysis tool. GenSel utilizes a MCMC algorithm to perform Bayesian inference using Gibbs sampling.</p> …
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Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data
… the study region. The ref.ICAR 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 …
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Bayesian Estimation of Material Properties in Case of Correlated and Insufficient Data
… experimental mechanics. 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. …
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Factor graphs and MCMC approaches to iterative equalization of nonlinear dispersive channels
… into forward-backward algorithm on hidden Markov model (HMM). The equalizer is implemented via the sum-product algorithm on the factor graph representation of the channel and receiver blocks. The second equalization strategy is based on Markov chain Monte Carlo (MCMC) methods. We typecast …
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Bayesian Balance Regression And Mediation Analysis For Microbiome Compositional Data
… introduce a Bayesian balance- regression and a Markov Chain Monte Carlo (MCMC) stochastic search algorithm to identify the compositional balance that is associated with the outcome. Specifically, we propose a random walk strategy in MCMC that explores the very large space of all possible balance …
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Bayesian generalized additive model selection
… auxiliary variable representations 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 …
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A Comparison of Two MCMC Algorithms for Estimating the 2PL IRT Models
<p>The fully Bayesian estimation via the use of Markov chain Monte Carlo (MCMC) techniques has become popular for estimating item response theory (IRT) models. The current development of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been …
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Bayesian risk management : "Frequency does not make you smarter"
… analysis we favor Bayesian statistics with its Markov Chain Monte Carlo (MCMC) simulation algorithm. It provides a full illustration of data-induced uncertainty beyond classical point-estimates. We calibrate twelve different stochastic processes to four years of CO2 price data. Besides, we …
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Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis
… (MLE), Bayesian Maximum A Priori (MAP), and Markov Chain Monte Carlo (MCMC) algorithm for physical models using relevant experimental data. The objective of the present work is to perform the sensitivity analysis of the code input (physical model) parameters in TRACE and calculate their …
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Cosmic acceleration and the coincidence problem
… the coincidence problem. We use a modified Markov Chain Monte Carlo (MCMC) technique together with the WMAP five year TT data to search for parameters allowing a second phase of acceleration. Despite extensive search we find no models that simultaneously fit the WMAP data and yield a second …
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Accelerating Bayesian Computation in Earth Remote Sensing Problems
… informed by the data. In the Bayesian approach, Markov chain Monte Carlo (MCMC) is implemented within this low-dimensional subspace to increase sampling efficiency. For an example retrieval, reducing the parameter dimension by a factor of 4 increased the effective sample size of the MCMC chain by …
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Parallel and distributed MCMC inference using Julia
… CPU cores as well as multiple machines, on Markov chain Monte Carlo (MCMC) inference algorithms. First, we take existing algorithms and implement them in Julia. We focus on MCMC inference using Continuous Piecewise-Affine Based (CPAB) transformations and a parallel MCMC sampler for Dirichlet …
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Quantifying uncertainty in computational neuroscience with Bayesian statistical inference
… of the Bayesian approach. 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 …
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Floor Plan Design Collaborator: A Data-Driven Approach to Assist Human Architects in Design Exploration
… generating new design 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 …
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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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State-Space Models and Latent Processes in the Statistical Analysis of Neural Data
… cell. These methods are based on sequential Monte Carlo techniques ("particle filtering"). We demonstrate, on model data, that these methods can recover the time course of excitatory and inhibitory synaptic inputs accurately on a single trial. In the fourth chapter we develop a more general …
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Evaluating The Efficiency of Markov Chain Monte Carlo Algorithms
<p>Markov chain Monte Carlo (MCMC) is a simulation technique that produces a Markov chain designed to converge to a stationary distribution. In Bayesian statistics, MCMC is used to obtain samples from a posterior distribution for inference. To ensure the accuracy of estimates using MCMC samples, …
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