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 20 for “"MCMC algorithm"”.
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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 Multi-GPU Compute Solution for Optimized Genomic Selection Analysis
<p>Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biological data. Some of these statistical models lend themselves nicely to standard high performance computing optimizations such as parallelism, while others do not. One such algorithm is Markov Chain …
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Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis
… A Priori (MAP), and Markov Chain Monte Carlo (MCMC) algorithm for physical models using relevant experimental data. The objective of the present work is to perform the sensitivity analysis of the code input (physical model) parameters in TRACE and calculate their uncertainties using an MLE, MAP …
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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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Bayesian empirical likelihood for quantile regression
… estimation of multiple quantiles. By using an MCMC algorithm in the computation, we avoid the daunting task of directly maximizing empirical likelihoods. The finite sample performance of the proposed method is investigated empirically, where substantial efficiency gains are demonstrated with …
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Bayesian analysis of multivariate stochastic volatility and dynamic models
… equations. We develop Markov Chain Monte Carlo (MCMC) algorithms that generate a posteriori restrictions on the elements of both the regression coefficients and the covariance matrix of the error term. Efficient parametrization of the time varying covariance matrices is studied using different …
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A multilevel logistic hidden Markov model for learning under cognitive diagnosis
… tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.
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First Step into A New Physics Realm: Search for the Majorana Nature of Neutrinos in the Inverted Mass Ordering Region
… analysis utilizing Markov Chain Monte Carlo (MCMC) algorithm. The lower limit for the 0𝜈𝛽𝛽 half-life of [formula] at 90% C.I., corresponding to the effective neutrino mass range of 38.4-160.0 meV, which is the first search in the inverted mass ordering region.
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Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection
… used to provide access to linear time filtering algorithms for performing inference. An MCMC algorithm based on Gibbs sampling with slice-sampling steps is provided and illustrated on simulated and real datasets. The MCMC algorithm exhibits excellent mixing and scalability.</p><p>Chapter 3 builds …
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Bayesian Hidden Markov Models for finding DNA Copy Number Changes from SNP Genotyping Arrays
… is proposed. A Markov chain Monte Carlo (MCMC) algorithm, with a forward-backward stochastic algorithm for sampling DNA copy number sequences, is developed for estimating model parameters. Numerous versions of Bayesian HMMs are explored, including a discrete-time model and different models …
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Statistical estimation problems in phylogenomics and applications in microbial ecology
… we explore some novel ways to use a Bayesian MCMC algorithm for jointly estimating alignment and phylogeny. The result is increased accuracy for large alignments, where the MCMC method alone would not be tractable. In the process, we identify a peculiar property of this Bayesian algorithm: it …
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Dynamic trading and behavioral finance
… affect portfolio performance. We propose an MCMC algorithm that is reasonably successful at estimating model parameters from simulated data, and look at the predictive ability of the model. We also provide preliminary results from looking at trading data obtained from a brokerage firm. We …
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Machine Learning and Variational Algorithms for Lattice Field Theory
… criticality. However, Markov chain Monte Carlo (MCMC) methods commonly used to evaluate the lattice-regularized path integral suffer from critical slowing down in this limit, restricting the precision of continuum extrapolations. Further difficulties arise when computing the energies and …
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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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Sequential methodology and applications in sports rating
… incorporating adaptive Markov chain Monte Carlo (MCMC) moves into the SMC update, it is possible to utilise the heuristic, computational and theoretical advantages of SMC to make gains in sampling efficiency. The new method is tested on the problem of Bayesian mixture analysis and found to …
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Rare events and dynamics in non-equilibrium systems
… 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 algorithm to sample the infinite-dimensional space of transition paths, which we call the *teleporter MCMC*. The algorithm was designed …
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Transport maps for accelerated Bayesian computation
… between (probability) measures. We introduce new algorithms that exploit these transformations as a fundamental tool for Bayesian inference. At the core of our approach is an ecient method for constructing transport maps using only samples of a target distribution, via the solution of a convex …
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Knowledge discovery with recommenders for big data management in science and engineering communities
… (CI) technologies and knowledge discovery driven algorithms will significantly enhance research and interdisciplinary collaborations in science and engineering. In this thesis, we demonstrate a novel recommender approach to discover latent knowledge patterns from both the infrastructure …
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Statistical models and inference for dynamic networks
… on the network. A Markov chain Monte Carlo (MCMC) estimation method within a Bayesian setting is presented. Several useful tools for the researcher arise from this estimation method. First, a method of predicting future relations, or edges, is given. Second, missing data can easily be …
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Estimating and Modeling Transpiration of a Mountain Meadow Encroached by Conifers Using Sap Flow Measurements
… and validation were completed using a MCMC approach via the DREAM<sub>(ZS)</sub> algorithm and a generalized likelihood (GL) function, enabling model parameter and total uncertainty assessment. We also used the model to inform transpiration scaling for the calibration period in select …