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 250 for “"MCMC"”.
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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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Ergodicity of Adaptive MCMC and its Applications
Markov chain Monte Carlo algorithms (MCMC) and Adaptive Markov chain Monte Carlo algorithms (AMCMC) are most important methods of approximately sampling from complicated probability distributions and are widely used in statistics, computer science, chemistry, physics, etc. The core problem to use …
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Parallel and distributed MCMC inference using Julia
… multiple machines, on Markov chain Monte Carlo (MCMC) inference algorithms. First, we take existing algorithms and implement them in Julia. We focus on MCMC inference using Continuous Piecewise-Affine Based (CPAB) transformations and a parallel MCMC sampler for Dirichlet Process Mixture Models …
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Flexible birth-death MCMC sampler for changepoint models
Diese Arbeit beschreibt eine flexible Architektur eines Markov Chain Monte Carlo Samplers, der Bayessche Inferenz für eine Vielzahl von Changepoint-Modellen erlaubt. Die Struktur dieser Klasse von Modellen besteht aus zwei stochastischen Prozessen. Der erste Prozess wird entweder direkt beobachtet …
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Rejection-Free and Partial Neighbor Search MCMC Algorithms
The Metropolis algorithm involves producing a Markov chain to converge in distribution to a specified target density $\pi$. To improve its efficiency, we can use the Rejection-Free version of the Metropolis algorithm, which avoids the inefficiency of rejections by evaluating all neighbors. …
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Efficient MCMC inference for remote sensing of emission sources
A common challenge in environmental impact studies and frontier exploration is identifying the properties of some emitters from remotely obtained concentration data. For example, consider estimating the volume of some pollutant that a chemical refinery releases into the atmosphere from measurements …
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Efficient MCMC inference for material detection and classification In tomography
… of Probabilistic Inference to implement MCMC sampling of realizations of the latent configuration conditioned on the measurements. We exploit conditional-independence properties of the graphical-model representation to sample many nodes in parallel and thereby render our sampling scheme …
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A Comparison of Two MCMC Algorithms for Estimating the 2PL IRT Models
… via the use of Markov chain Monte Carlo (MCMC) techniques has become popular for estimating item response theory (IRT) models. The current development of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been used with fitting various …
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Minimal I-MAP MCMC for scalable structure discovery in causal DAG models
… current state-of-the-art methods such as order MCMC are faster than previous methods but prevent the use of many natural structural priors and still have running time exponential in the maximum indegree of the true directed acyclic graph (DAG) of the BN. We here propose an alternative posterior …
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Factor graphs and MCMC approaches to iterative equalization of nonlinear dispersive channels
… strategy is based on Markov chain Monte Carlo (MCMC) methods. We typecast the problem of executing forward-backward algorithm on HMM into an MCMC domain problem and develop four different types of MCMC equalizers.
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A study of Population MCMC for estimating Bayes Factors over nonlinear ODE models
… schedule which should be employed for Population MCMC, and several ideas for future research extending this work are also discussed.
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Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy
… inference (VI) and Markov Chain Monte Carlo (MCMC) are two major techniques with their own merits and limitations. In the first part of the thesis, we aim to design efficient approximate inference algorithms by combining VI and MCMC (particularly, stochastic gradient MCMC (SG-MCMC)). The first …
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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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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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Fast EM Based Posterior Approximation for IRT Item Parameters
… as estimated through Markov chain Monte Carlo (MCMC). MCMC estimation is typically very computationally intensive for IRT models and does not result in the same parameter estimates as the MBME method. It is thus not currently used operationally. The goal of this research is to propose a …
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Integral geometry, Hamiltonian dynamics, and Markov Chain Monte Carlo
… 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 [pi]. However, if [pi] has …
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Slice Sampling with Multivariate Steps
Markov chain Monte Carlo (MCMC) allows statisticians to sample from a wide variety of multidimensional probability distributions. Unfortunately, MCMC is often difficult to use when components of the target distribution are highly correlated or have disparate variances. This thesis presents three …
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Bayesian inference of chemical reaction networks
… proposals for Markov chain Monte Carlo (MCMC). We then introduce a sensitivity-based determination of move types which, when combined with the network-aware proposals, yields further sampling efficiency. These algorithms are tested on example problems with up to 1000 plausible models. We …
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Graphlet based network analysis
… sampling, we propose Markov Chain Monte Carlo (MCMC) sampling based methods for triple and graphlet analysis. Proposed triple analysis methods, Vertex-MCMC and Triple-MCMC, estimate triangle count and network transitivity. Vertex-MCMC samples triples in two steps. First, the method selects a …
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