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 10 of 10 for “"Metropolis-Hastings algorithm"”.
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Modeling Transition Probabilities for Loan States Using a Bayesian Hierarchical Model
… probabilities are estimated using MCMC and the Metropolis-Hastings algorithm.
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Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data
… Bayesian inference in conjunction with the Metropolis-Hastings algorithm. The Bayesian posterior probability is computed for two distinct cases. One case assumes the replicates are independent events, the other assumes the replicates are not independent events (using a hierarchical …
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An Adaptive Bayesian Approach to Bernoulli-Response Clinical Trials
… rules inside the Markov chain Monte Carlo algorithm. An order restricted Metropolis-Hastings algorithm is implemented to account for these limitations. Modeling clinical trials in a Bayesian framework allows the experiment to be adaptive. In this adaptive design batches of subjects are …
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Graphical methods in prior elicitation.
… that are presented in graphical form. The algorithms then convert these selections into a prior distribution on the parameter(s) of interest. After discussing each elicitation method, we propose a variation on the Metropolis-Hastings algorithm that provides support for the underlying …
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Modeling spatial patterns of mixed-species Appalachian forests with Gibbs point processes
… (MCMC) methods; in particular, a reversible-jump Metropolis-Hastings algorithm with birth, death, and shift proposals was utilized. Parameters for the models were estimated by a Bayesian inferential procedure that utilizes MCMC methods to draw samples from the Gibbs posterior density. Two …
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Bayesian Alignment Model for Analysis of LC-MS-based Omic Data
… of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS). Chapter 3 extends the model to integrate complementary information that better captures the variability in …
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Bayesian analysis of finite mixture distributions using the allocation sampler
… (1987) and Gelfand and Smith (1990)) and the Metropolis-Hastings algorithm (Hastings (1970)). However, the number of components in the model can also be considered an unknown and an object of inference. Richardson and Green (1997) and Stephens (2000a) both describe Bayesian methods to sample …
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Multivalent Random Walkers:A computational model of superdiffusive transport at the nanoscale
… chain Monte Carlo simulation techniques. The Metropolis-Hastings algorithm approximates the motion of a walker's body and legs at a mechanical equilibrium, while the kinetic Monte Carlo algorithm simulates the transient chemical dynamics of the walker stepping across the surface sites. Using …
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Some Selective Inference and Optimization Methods for Reliable Causal Inference
… we employ rejection sampling or the random-walk Metropolis-Hastings algorithm. Furthermore, confidence intervals for a homogeneous treatment effect can be constructed via inversion of tests. To mitigate the risk of disconnected confidence intervals, we propose the use of hold-out units. Lastly, …
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Statistical algorithms using multisets and statistical inference of heterogeneous networks
… improvement of the efficiency for the EM algorithm and the MCMC method, and statistical analysis for heterogeneous networks. The expectation-maximization (EM) algorithm is widely used in computing the maximum likelihood estimates when the observations can be viewed as incomplete data. …