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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 40 for “"Gibbs sampler"”.
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Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler
… of Thurstonian ranking models based on Gibbs sampling methods. Monte Carlo studies demonstrate that the Gibbs sampler is applicable to ranking problems with a large number of objects. To improve the efficiency of the Gibbs sampler for estimating constrained and unconstrained Thurstonian …
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A Comparison of Bayesian Estimation Techniques in a Multidimensional Two-Parameter Partial Credit Item Response Model
… Markov Chain Monte Carlo (MCMC) algorithms: Gibbs Sampler and Hamiltonian Monte Carlo-No-U-Turn-Sampler (HMC-NUTS) for M2PPC models' parameter estimation. It compared the estimation accuracy and computing speed in different combinations of situations, including prior choices, test lengths, …
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A nested random effects model analysis of child survival in Malawi
… parameters of the model are estimated using the Gibbs 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 …
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Evaluating The Efficiency of Markov Chain Monte Carlo Algorithms
… we use simulation studies to demonstrate how the Gibbs sampler and the Metropolis-Hasting algorithm works and how MCMC diagnostic tests are used to check for MCMC convergence. We investigated and compared the efficiency of different MCMC algorithms fit to a linear and a spatial model. Our results …
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Efficient Bayesian analysis of spatial occupancy models
… variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the single-season spatial occupancy model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial …
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Clustering Analysis of Zernike Coefficients Through Quantile Regression
… Zernike coefficients and pupil size. We employ Gibbs sampler and adaptive rejection Metropolis sampling to infer the parameters for each cluster. Bayesian information criterion (BIC) combined with a measure of uncertainty are used to determine the number of clusters. A comparison of likelihoods …
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Bayesian Regression Inference Using a Normal Mixture Model
… We implement our model computationally using the Gibbs sampler algorithm and apply it to a dataset of differences in time measurement between two clocks. The dataset has ``good" time measurements and ``bad" time measurements that were associated with the two components of our mixture model. From …
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GPU-accelerated Inference for Discrete Probabilistic Programs
… in JAX that supports variable elimination and Gibbs sampling, and (2) a modeling DSL with a compiler that lowers programs to the factor graph IR. Our system enables significant performance optimizations through static analysis of the factor graph structure. Variable elimination is optimized by …
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Regression modeling: Latent structure, theories and algorithms
… We have Monte-Carlo-Newton-Raphson Algorithm, Gibbs Sampler, EM algorithm and algorithm to evaluate weighted sum $\chi\sp2$ quantile. The associated theories are provided. In scaled link model, some sensitivity studies are made.
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Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market
… African financial market data. A single move Gibbs sampler is used to sample parameters from the posterior distribution. Volatility is used as measure of an asset's risk. It is particularly important in risk management, derivatives pricing, and portfolio selection. When pricing derivatives it …
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Bayesian regularized quantile mixed models for longitudinal studies
… by incorporating the random effects. The Gibbs sampler has been developed along with the Markov Chain Monte Carlo (MCMC). We have established the advantage of the proposed method over multiple competing methods in extensive simulation studies and a high-dimensional lipidomics study with …
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Variance Components of litter size in Icelandic farmed mink (Neovison vison)
… in the pedigree. Two methods were employed, MCMC Gibbs sampler method and REML. The two different methods gave similar results. Heritability (0.03-0.06) and repeatability (0.08-0.16) was lower than in previous studies. EVA software was used to estimate pedigree structure. Average inbreeding was …
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Bayesian Uncertainty Quantification while Leveraging Multiple Computer Model Runs
… process parameters, these are input to a Gibbs sampler which provides posterior distributions for parameters of interest. These samples are used to generate predictions which provide uncertainty quantification for a given computer model run (e.g., tropical cyclone precipitation forecast). …
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Novel Monte Carlo Approaches to Identify Aberrant Pathways in Cancer
… pathways. We propose a robust method, called GibbsOS, to identify condition specific gene regulatory patterns between transcription factors and their target genes. A Gibbs sampler is employed to sample target genes from the marginal function of outlier sum of regression t statistic. Numerical …
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A Random-Linear-Extension Test Based on Classic Nonparametric Procedures
… test can be simply implemented using a Gibbs Sampler to generate a random sample of complete orderings. Given a complete ordering, standard nonparametric methods, such as the Wilcoxon rank-sum test, can be applied, and the corresponding test statistics and rejection regions can be …
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Monte Carlo integration in discrete undirected probabilistic models
… other partitioned sampling schemes and the naive Gibbs sampler, even in cases where loopy belief propagation fails to converge. We prove that tree sampling exhibits lower variance than the naive Gibbs sampler and other naive partitioning schemes using the theoretical measure of maximal …
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Competitive regression.
… novel algorithms (Online Shrinkage via Limit of Gibbs sampler (OSLOG), Competitive Iterated Ridge Regression (CIRR) and Competitive Normalised Least Squares (CNLS)) are derived and analysed. The development of these algorithms is driven by Kolmogorov complexity (known also as “competitive …
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Mitigating timing noise in ADCs through digital post-processing
… estimator and several variants on the Gibbs sampler that all approach the Bayes least squares estimate are designed. These estimators are compared in performance to the optimal linear estimators derived without taking jitter into account.
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Combining Probabilistic Shape-from-Shading & Statistical Facial Shape Models
… (FB8) distributions are sampled using Gibbs sampling to give normal distributions on the tangent plane. These normal distributions are in turn combined with normal distributions arising from statistical models of facial shapes. Chapter 2 gives a brief review of Shape-from-Shading and …
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Double diffraction of quasiperiodic structures and bayesian image reconstruction
… multiplicity prior distribution, and use Gibbs sampling to reconstruct the latent image. In contrast with the traditional entropy prior, our modified multiplicity prior avoids the Sterling's formula approximation, incorporates an Occam's razor, and automatically adapts for the information …
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