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 19 of 19 for “"MCMC method"”.
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Use of spatial models and the MCMC method for investigating the relationship between road traffic pollution and asthma amongst children
… to analyse the relationship. A discretized MCMC (Markov Chain Monte Carlo) is developed so that it can be used to estimate parameters and to do inference on a very complex posterior density function. It extends the simulated tempering method to 'multi-dimension temperature' situation. We use …
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Mcmc- Based Optimization And Application
… we study the theory of Markov Chain Monte Carlo (MCMC) and its application in statistical optimization. The MCMC method is a class of evolutionary algorithms for generating samples from given probability distributions. In the thesis, we first focus on the methods of slice sampling and simulated …
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Three Statistical Problems With Imprecisely or Incompletely Observed Data
… which poses challenges to the usual statistical methods. This thesis consists of three studies in this general area. First, we study linear calibration of a crude device to a more accurate one. Second, we use the Markov Chain Monte Carlo (MCMC) method to handle a grouped independent variable in a …
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Objective Assessment Methods for List Mode Imaging System
… approximation by use of supervised learning methods on binned data, where a single-layer neural network (SLNN) is used to approximate the HO test statistic. The upper bound set by the ideal observer can be used for the optimization of the imaging system and the performance measure of other …
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Population Pharmacokinetics of Therapeutic Monoclonal Antibodies: Examples and Estimation Method Performance Differences
… and the evaluation of different estimation methods for population PK modeling of therapeutic mAbs with nonlinear PK characteristics.</p> <p> Cetuximab is a therapeutic mAb directed against the epidermal growth factor receptor and is indicated in the treatment of squamous cell carcinoma of …
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Bayesian Estimation of Multi-unidimensional Graded Response IRT Models
… via the use of Markov chain Monte Carlo (MCMC). The performance of the proposed model was evaluated using the Monte Carlo simulations. It was further compared with conventional GRMs under simulated and real test situations. Results from simulation studies as well as a real data example …
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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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A nested random effects model analysis of child survival in Malawi
… sampler, a Bayesian Markov Chain Monte Carlo (MCMC) method. We believe this to be the first time the method has been used for this type of data. The parameters are also estimated using the expectation-maximisation (EM) algorithm, a method that has previously been used to analyse this type of …
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Statistical estimation problems in phylogenomics and applications in microbial ecology
… statistically consistent by several different methods, but those proofs rely on two separate and potentially shaky assumptions: that every species appears in the data for every gene (i.e., there is no missing data), and that since gene tree estimation is itself consistent, the gene trees used …
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Physics-Based Statistical Learning in Thermoacoustics
… Then we use the Markov Chain Monte Carlo (MCMC) method to infer the parameters of a linear acoustic model that is driven by the thermoacoustic mechanism and damped by visco-thermal dissipation and by radiation from the ends of the tube. We perform experiments only on the fully-assembled …
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Mathematical Analysis of Upscaling Well Log Data from Sonic to Seismic Resolution
… seismic observations. Various upscaling methods have been published and discussed, such as the Simple Averaging method, the Backus Averaging method, the Pair Correlation Function “PCF” method, the General Singular Approximation “GSA” method, and the Markov Chain Monte Carlo “MCMC” method. …
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Statistical algorithms using multisets and statistical inference of heterogeneous networks
Computational statistics, including methods such as Markov chain Monte Carlo (MCMC), bootstrap, approximate Bayesian computation, is an important part in modern statistics and has been widely used in many areas, such as Bayesian statistics, computational biology, and computational physics. In this …
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Graphlet based network analysis
… more comprehensive class of graphlet analysis methods works with graphlets of specific sizes—graphlets with three, four or five nodes ({3, 4, 5}-<em>Graphlets</em>) are particularly popular. For all the above analysis tasks, excessive computational cost is a major challenge, which becomes …
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Bayesian Balance Regression And Mediation Analysis For Microbiome Compositional Data
… regression and a Markov Chain Monte Carlo (MCMC) stochastic search algorithm to identify the compositional balance that is associated with the outcome. Specifically, we propose a random walk strategy in MCMC that explores the very large space of all possible balance defined from high …
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Rare events and dynamics in non-equilibrium systems
… over the TPE. We develop spectral Ritz methods to efficiently find minimisers of this action, and to construct quasipotentials of steady-state distributions, and we test our algorithm on a number of benchmark systems. To study the TPE in the finite temperature regime, we develop an MCMC …
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The application of Bayesian adaptive design and Markov model in clinical trials
… earlier. In this research, Bayesian adaptive method is used as a new and useful approach that applies to phase IB and phase II dose-finding clinical trials to evaluate safety and efficacy of the study treatment. Response model and Normal Dynamic Linear Models (NDLMs) are applied in stages 1-4. …
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Topics on statistical design and analysis of cDNA microarray experiment
… which is based on the simulated annealing method to find optimal or near-optimal designs with both biological and technical replicates. In the second subtopic, we discuss how to apply Q-criterion for the factorial design of microarray experiments. In the third subtopic, we suggest an …
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Bayesian-based simulation model validation for spacecraft thermal systems
… proposes a Bayesian-based Model Validation (BMV) methodology as a tailored framework that combines the state of the art model validation methods within the fields of Uncertainty Quantification (UQ) and Design of Experiments (DOE) to improve the thermal model validation process. In BMV, model …
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Markovian and stochastic differential equation based approaches to computer virus propagation dynamics and some models for survival distributions
… SEIR model using Markovian approach, the method of maximum likelihood estimation for model parameters of interest are derived. The method of least squares is used to estimate the model parameters of interest in the multi-group stochastic SEIR-SDE model, based on stochastic differential …