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 631 for “"Markov Chain"”.
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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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Extensions of Markov Chain Marginal Bootstrap
The Markov chain marginal bootstrap (MCMB) is a new bootstrap method proposed by He and Hu (2002) for constructing confidence intervals or regions based on likelihood equations. It is designed to ease the computational burden of bootstrap in high-dimensional problems. It differs from the usual …
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Markov Chain Marginal Bootstrap for Generalized Estimating Equations
… estimates. In this thesis, we extend the Markov chain marginal bootstrap (MCMB) (He and Hu, 2002) to statistical inference for robust GEE estimators with longitudinal data, allowing the estimating functions to be non-smooth and the responses correlated within subjects. By decomposing the …
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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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A Markov chain approach to electrocardiogram modeling and analysis
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1985.
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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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Airline Dynamic Offer Creation Using A Markov Chain Choice Model
… these dynamic offer creation problems using a Markov chain choice model (MCCM). We discover that it is particularly suited to solving the bundle pricing problem, which also has potential applications in other industries. We also extend the MCCM to solve joint pricing and assortment optimization …
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A Markov chain approach for analyzing Palmer drought severity index
… depicts prolonged abnormal dryness or wetness. A Markov chain model was developed to analyze the likelihood of occurrences of the seven types of weather spells, defined by the National Oceanic and Atmospheric Administration (NOAA). The spells are classified, using the PDSI computed monthly by the …
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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
… a fixed number of samples. Our framework uses Markov Chain Monte Carlo and Kernel Density Estimation algorithms to create a non-parametric importance sampling distribution that can form lower variance estimates of the recourse function. We demonstrate the increased accuracy and efficiency of …
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A hierarchical Markov chain based solver for very-large-scale capacitance extraction
… thesis presents two hierarchical algorithms, FastMarkov and FD-MTM, for computing the capacitance of very-large-scale layout with non-uniform media. Fast- Markov is Boundary Element Method based and FD-MTM is Finite Difference based. In our algorithms, the layout is first partitioned into small …
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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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Flags of Caution for Future Downturns in the Housing Market Prediction Using the Markov Chain Model
… burst the real estate bubble. In this study the Markov Chain Model was used as a forecasting tool to evaluate the status of home mortgages and to demonstrate the capability to predict future housing economic crises. Statistical data from both eras were gathered and shown in a transition matrix.
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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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A bacterial algorithm for surface mapping using a Markov modulated Markov chain model of bacterial chemotaxis
… objective function. Towards that end, a discrete Markov modulated Markov chains model of the chemotaxis pathway is described and used. Results from simulations using one- and two-dimensional test surfaces show that the software agents, referred to as bacterial agents, and the surface mapping …
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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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