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 13 of 13 for “"Monte Carlo Markov Chain"”.
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Bayesian Parameter Estimation on Three Models of Influenza
… complex models. We use Bayesian inference and Monte Carlo Markov Chain methods to estimate the underlying densities of the parameters (assumed to be continuous random variables) for three models of influenza. We discuss the advantages and limitations of parameter estimation using these methods. …
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SALT spectroscopy and classification of supernova spectra using Bayesian techniques
… selection, fitting the entire spectrum with Monte Carlo Markov Chain methods which allow estimation of the entire parameter posterior distributions, and hence principled statistical analysis even at low signal-to-noise. After extensive testing of SuperNovaMC against simulations and literature …
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FireFly: A Bayesian Approach to Source Finding in Astronomical Data
… In this thesis, we implement nested sampling and Monte Carlo Markov Chain (MCMC) techniques to develop a new Bayesian source finding technique called FireFly. FireFly employs a technique of switching ‘on’ and ‘off’ sources during sampling to deal with the fact that we don’t know how many true …
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Denoising via Empirical Bayesian Pursuit
… mixture (GM) signal model is considered. Monte Carlo Markov-chain methods are developed for converging to optimal model parameter estimates, which are then used to generate a signal-dependent Wiener filter for denoising. Finally, the solution to the latent GM model problem is briefly …
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Transit timing with fast cameras on large telescopes
… literature data were jointly fit using a Monte Carlo Markov Chain method, providing accurate new values for the planetary radius and other parameters. Transit ephemerides have been updated and transit midtimes have been investigated for potential transit timing variations (TTVs) caused by …
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Objective Assessment Methods for List Mode Imaging System
… observer has never been easy. For binned data, a Monte Carlo Markov Chain(MCMC) method was proposed. Here in this work, an example of approximating the ideal observer for list mode data given a background known statistically signal known exactly model is provided in Chapter 5. Two methods, the …
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Algorithms and Algorithmic Barriers in High-Dimensional Statistics and Random Combinatorial Structures
… including the class of stable algorithms and the Monte Carlo Markov Chain type algorithms. The former is a rather powerful abstract class that captures the implementation of several important algorithms including the approximate message passing and the low-degree polynomial based methods. Our …
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Exoplanet atmospheric exploration and categorization through transmission spectroscopy
… clouds. In this study, I investigated, using a Monte Carlo Markov Chain atmospheric retrieval algorithm, whether planetary parameters could compensate for each other to create the apparent flatness in the observed transmission spectrum of GJ 1214b. My analysis confirms the conclusions of …
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Sequential bayesian filtering for spatial arrival time estimation
… arrays of spatially separated receivers. Using Monte Carlo simulations, we perform an evaluation of our method and compare it to conventional Maximum Likelihood (ML) estimation. The comparison demonstrates an advantage in using the proposed approach, which can be employed as a pre-inversion tool …
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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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Ensemble filtering for state space models
… of the method is motivated by the particle Markov chain Monte Carlo method proposed by Andrieu et al. (2010). Often, the combination of particle filters in batches outperforms the standard particle filter. Parallel computing techniques can be easily adapted to make the implementation fast. …
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Large-scale Bayesian computation using Stochastic Gradient Markov Chain Monte Carlo
Markov chain Monte Carlo (MCMC), one of the most popular methods for inference on Bayesian models, scales poorly with dataset size. This is because it requires one or more calculations over the full dataset at each iteration. Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a …
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Formally justified and modular Bayesian inference for probabilistic programs
… semantics corresponding to several variants of Markov chain Monte Carlo and Sequential Monte Carlo methods and formally prove a notion of correctness for these algorithms in the context of probabilistic programming. We also show that the semantic construction can be directly mapped to an …