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 11 of 11 for “"Monte Carlo estimation"”.
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Variational Monte Carlo estimation of the dissociation energy of CuH using correlated sampling
… approach to treating large Z systems by quantum Monte Carlo has been developed. It naturally leads to notion of the 'valence energy'. Possibilities of the new approach has been explored by optimizing the wave function for CuH and Cu and computing dissociation energy and dipole moment of CuH using …
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A Bayesian Markov Chain Monte Carlo approach to the generalized graded unfolding model estimation: the future of non-cognitive measurement
… proposed in the literature, the only model with estimation software available to the public is the generalized graded unfolding item response model (GGUM) and its corresponding software GGUM2004. However, this software sometimes encounters problems due to the marginal maximum likelihood (MML) …
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Sustainability Strategies in Supply Chain Management
… indicators database and Markov chain Monte Carlo estimation procedure. The results provide support that resource-based view explains the maximum differential environmental performance of firms as opposed to industry-based view or institutional theory.</p>
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Deep Generative Models and Biological Applications
… theoretical justification to the convergence of Monte Carlo estimation in our algorithm. </p><p>Then, we apply the amortized variational inference to a dynamic modeling application in flu diffusion task. </p><p>Compared with traditional approximate Gibbs sampling algorithm, we make less …
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Model-based clustering for multivariate time series of counts
… To estimate the model parameters, a new Monte Carlo Estimation Maximization (MCEM) algorithm is developed. The Monte Carlo sampling eliminates complex recursion formulas needed for calculating the probability function of the multivariate Poisson. The algorithm is easily adapted for …
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Exploring nonlinear regression methods, with application to association studies
… fail. Sparse Partitioning relies on Markov chain Monte Carlo estimation, which limits the size of problem on which it can be used. Therefore, in Chapter 5, I propose a deterministic version of the method which, although less powerful, is not affected by convergence issues. In Chapter 6, I describe …
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Sequential Monte Carlo Methods with Applications to Positioning and Tracking in Wireless Networks
… models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks. The aim of the first paper is to study the performance of particle filtering techniques in mobile positioning using signal strength measurements. Two …
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Optimizing wastewater treatment sampling strategies through Markov Chain Monte Carlo Bayesian inference in Activated Sludge Model No. 3
… remains constrained by challenges in parameter estimation and uncertainty quantification under realistic monitoring conditions. This thesis develops a comprehensive Bayesian inference framework for Activated Sludge Model No. 3 that integrates sensitivity screening, identifiability diagnostics, …
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Variance-reduced simulation of lattice Markov chains
… of this dissertation is on reducing the cost of Monte Carlo estimation for lattice-valued Markov chains. We achieve this goal by manipulating the random inputs to stochastic processes (Poisson random variables in the discrete-time setting and Poisson processes in continuous-time) such that they …
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A Dynamic Space-Time Panel Data Model of State-Level Beer Consumption
A dynamic space-time panel data model containing random effects is used to examine state-level beer consumption over the period of 1970 to 2007 for the 48 contiguous US states and the District of Columbia. A valuable aspect of dynamic space-time panel data models is that the parameter estimates …
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Efficiently Estimating Survival Signature and Two-Terminal Reliability of Heterogeneous Networks through Multi-Objective Optimization
… and simulation-based approximations, such as Monte Carlo algorithms, are generally required. Nonetheless, the computation of the network's signature poses a majorchallenge in terms of computational time, especially when considering large, heterogeneous networks. Motivated by this, we propose a …