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 71 for “"Gibbs sampling"”.
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A Gibbs sampling approach to maximum a posteriori time delay and amplitude estimations
… efficient, however, is not a trivial task. A Gibbs Sampling Monte Carlo technique is proposed to recover these arrivals and their features. The method is tested on synthetic data as well as data from the Haro Straight experiment for the estimation of the number of arrivals, the amplitude and …
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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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Sampling architectures for probabilistic inference
… (LDA). Our work focuses on inference via sampling methods, in particular, Markov chain Monte Carlo (MCMC) methods. Roughly speaking, we generate samples from the distribution of labels implied by the structure of the graphical model, and use results computed from the samples to approximate …
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A Combined Motif Discovery Method
… method that uses mutual information and Gibbs sampling was developed. A new scoring schema was introduced with mutual information and joint information content involved. Simulated tempering was embedded into classic Gibbs sampling to avoid local optima. This method was applied to the 18 …
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Modeling Correlated Ordinal Data: Marginal and Conditional Approaches
… for the GEE computations and WinBugs to perform Gibbs sampling for the Bayesian analysis. Analyses of randomized controlled longitudinal data and randomized controlled surgical data are used to illustrate the features of the class of models.
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Consistent anticipatory route guidance
… to large-scale problems. Methods included Gibbs sampling for highly stochastic maps; generalizations of functional iteration for deterministic maps; and the MSA and Polyak iterate averaging method for "noisy" (deterministic plus disturbance) maps. A guidance-oriented dynamic traffic …
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A Comparison of Two MCMC Algorithms for Estimating the 2PL IRT Models
… of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been used with fitting various IRT models, the latter is relatively new, calling for the research to compare it with other algorithms. The purpose of the present study is to …
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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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Bayesian time series models and scalable inference
… a special case of a highly parallelizable sampling strategy we refer to as Hogwild Gibbs sampling. Thorough empirical work has shown that Hogwild Gibbs sampling works very well for inference in large latent Dirichlet allocation models (LDA), but there is little theory to understand when it …
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Multi-Way Block Models
… to variational Bayesian, collapsed Gibbs sampling, collapsed variational Bayesian, and expectation propagation approaches. Comparative simulation studies show that the four implementation algorithms achieve meaningful parameter estimates for the latent membership and block structure …
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Statistical Analysis of the Cosmic Microwave Background: Power Spectra and Foregrounds
… and deal with polarized data, the future of the Gibbs sampling approach shows great promise.
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Fast and efficient approaches to large-scale occupancy models
… accuracy of the approximations improves as the sampling occasions increase. Approximate methods could be implemented when the detection probability is at least 0.5 and when there are at least three sampling occasions. The link between logistic regression and occupancy modelling was exploited to …
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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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Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura
… Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates
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Computational Bayesian methods applied to complex problems in bio and astro statistics.
… Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a …
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A Multi-GPU Compute Solution for Optimized Genomic Selection Analysis
… algorithm to perform Bayesian inference using Gibbs sampling.</p> <p>Optimizing an MCMC algorithm is a difficult problem because it is inherently sequential, containing a loop carried dependence between each Markov Chain iteration. The optimization presented in this thesis utilizes GPU …
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Detecting intervention effects with a cognitive diagnostic model for learning trajectories
… Bayesian modeling formulation is presented, and Gibbs sampling algorithm is proposed for parameter estimation. Simulation study results show that the proposed model provides accurate estimation of intervention effects and reliable recovery of students' latent attributes.
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Agreement Webs
… model fitting by both constrained likelihood and Gibbs sampling. Estimation and inference on rater-specific sensitivity and specificity are also discussed. These methods are illustrated using a mammography example (Beam et al, Arch. Intern. Med.,2003) where 148 patient mammograms are each assessed …
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Modeling Dynamic Objects in Scenes with Generative Particle Systems
… inference algorithm based on parallelized block Gibbs sampling to recover stable particle motion and rigid groupings. Our model provides a tractable, object-centric generalization of as-rigidas-possible (ARAP) regularizers used in motion tracking. To assess alignment with human perceptual …
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Bayesian generalized additive model selection
… enable the Markov chain Monte Carlo (MCMC) sampling reduce to the Gibbs sampling or to slice sampling. To improve computational scalability and speed, we also derive the mean field variational Bayes (MFVB) algorithms under the Laplace-Zero and Grouped Lasso-Zero priors. The GAM selection …
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