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 332 for “"Markov chain Monte Carlo"”.
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Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo
This thesis consists ideas of two new population Markov chain Monte Carlo algorithms and an automatic proposal mechanism for the Reversible jump Markov chain Monte Carlo algorithm.
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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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Integral geometry, Hamiltonian dynamics, and Markov Chain Monte Carlo
… and graph theory to the design and analysis of Markov chain Monte Carlo (MCMC) algorithms. MCMC algorithms are used to generate samples from an arbitrary probability density [pi] in computationally demanding situations, since their mixing times need not grow exponentially with the dimension of …
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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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Markov chain Monte Carlo and its applications to phylogenetic tree construction
… an inference algorithm for this model based on a Markov chain Monte Carlo method in order to overcome the computational complexity inherent in the problem. Initial results show potential advantages over methods for phylogenetic tree estimation that do not make use of the species phylogeny.
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Markov chain Monte Carlo methodoloy for inference with generalised linear spatial models
… techniques and a common approach is the use of Markov chain Monte Carlo methods (MCMC). However, the correlation between the components of the latent process and the correlation between the latent process and the model parameters generally hinders the performance of any MCMC scheme which updates …
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The Markov chain Monte Carlo approach to importance sampling in stochastic programming
… function is estimated using quadrature rules or Monte Carlo methods. Although Monte Carlo methods present numerous computational benefits over quadrature rules, they require a large number of samples to produce accurate results when they are embedded in an optimization algorithm. We present an …
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Applications of Markov Chain Monte Carlo methods to continuous gravitational wave data analysis
… has been developed. The work is based on the Markov Chain Monte Carlo algorithm and features enhancements specifically targeted to this problem. The algorithm is tested on both synthetic data and hardware injections in the LIGO Hanford interferometer during its third science run ("S3''). By …
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A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo
… In this work we present an algorithm merging Markov Chain Monte Carlo methods with the EM algorithm to find qualitatively better solutions for the clustering problem. We present brief introductions to two popular clustering algorithms, K-Means and EM, as well as the Markov Chain Monte Carlo …
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Stochastic Optimization Powered by Markov Chain Monte Carlo: Mixed-Integer Nonlinear Programming for Communications Network Scheduling
<p>Markov chain Monte Carlo methods are known for their effectiveness with a multitude of complex mathematical problems, including those in mixed spaces of continuous and discrete components. In this dissertation, we examine variations of complicated scheduling problems involving allocating …
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Deterioration Model for Ports in the Republic of Korea using Markov Chain Monte Carlo with Multiple Imputation
… develop deterioration model. In this research, Markov model using Markov chain theory, one of the Stochastic methods, is used to develop deterioration model for ports in South Korea. Markov chain is a probabilistic process among states. i.e., in Markov chain, transition among states follows some …
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Optimizing wastewater treatment sampling strategies through Markov Chain Monte Carlo Bayesian inference in Activated Sludge Model No. 3
… screening, identifiability diagnostics, and Markov chain Monte Carlo estimation to deliver robust parameter estimates and predictive uncertainty bounds across diverse monitoring designs. The methodology combines normalised sensitivity analysis to identify negligible parameters, profile …
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A Bayesian Markov Chain Monte Carlo approach to the generalized graded unfolding model estimation: the future of non-cognitive measurement
… a state-of-the-art estimation method––Bayesian Markov Chain Monte Carlo estimation. A series of studies were conducted to test the estimation accuracy of the new software. The results clearly showed that the Bayesian MCMC estimation method outperformed the traditional MML method, in terms of …
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On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions
… in knowledge discovery scales exponentially. Markov chain Monte Carlo (MCMC) is a broad class of powerful algorithms, typically used for Bayesian inference. Despite their variety and versatility, these algorithms rarely become mainstream workhorses because they can be difficult to implement. …
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Qualitative and quantitative convergence results for randomised integration methods
… methods based on either, randomised Quasi-Monte Carlo, or (adaptive) Markov chain Monte Carlo methods are studied. Depending on the underlying integration problem we show qualitative and quantitative results, which ensure the asymptotic correctness of an algorithm or provide explicit error …
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A multiscale framework for Bayesian inference in elliptic problems
… standard method of sampling this distribution is Markov chain Monte Carlo which can become inefficient in high dimensions, wasting many evaluations of the likelihood function. In many applications the likelihood function involves the solution of a partial differential equation so the large number …
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Pricing stochastic volatility models using random grids
… calibration, finite difference solution and Markov-chain MonteCarlo simulation based on the random grids approach. This dissertation provides a review and implementation of this random grids approach for pricing under the Heston model as well as the stochastic local volatility model. …
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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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Linkage Based Dirichlet Processes
… researchers' desire for 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 …
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