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 14 of 14 for “"Hamiltonian Monte Carlo."”.
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General and Efficient Bayesian Computation through Hamiltonian Monte Carlo Extensions
<p>Hamiltonian Monte Carlo (HMC) is a state-of-the-art sampling algorithm for Bayesian computation. Popular probabilistic programming languages Stan and PyMC rely on HMC’s generality and efficiency to provide automatic Bayesian inference platforms for practitioners. Despite its wide-spread use and …
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Co-generation with GANs using AIS based HMC
… we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm. The presented approach significantly outperforms classical gradient based methods on a synthetic and on the CelebA and LSUN datasets.
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Computational Bayesian methods applied to complex problems in bio and astro statistics.
… missing data, and fit the model with Hamiltonian Monte Carlo in simulation to analyze how estimates of a parameter of interest change across sample sizes.
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Advancing Normalising Flows to Model Boltzmann Distributions
… tuning procedure for the sampling method Hamiltonian Monte Carlo. It runs gradient-based optimisation on a variational objective. Hamiltonian Monte Carlo can be applied on top of a normalising flow to enhance its samples. With the improved hyperparameters thanks to our method, we can …
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Multilevel modelling of determinants of contraceptive method choice among women in South Africa
… strengthened by the use of the state of the art Hamiltonian Monte Carlo algorithm (HMC), as implemented in the RStan package in the R statistical software. The Bayesian nal model was selected based on Watanabe{Akaike information criterion (WAIC), which has been shown to outperform conventional …
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Mathematical Tools for Discontinuous Dynamical Systems
… Next, a new nonsmooth formulation of Hamiltonian dynamics for Hamiltonian systems with nonsmooth potential energy is developed, using lexicographic differentiation to derive a system of discontinuous differential equations, and theoretical results are developed. Using this nonsmooth …
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Multivariate Nonstationary Time Series: Spectrum Analysis and Dimension Reduction
… and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. The second part of the dissertation aims to shed lights on the usefulness of contemporaneous aggregation for high--dimensional time series analysis, …
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Charge carrier transport and recombination in semiconductors: Insights from statistical analysis and machine learning techniques
… This physically motivated model based on Hamiltonian Monte Carlo and Bayesian Inference outperforms existing fitting techniques, providing accurate parameter estimation and error estimates, thus advancing time–resolved photoluminescence analysis for optoelectronics and beyond. In Chapter …
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Probabilistic Machine Learning for Circular Statistics: Models and inference using the Multivariate Generalised von Mises distribution
… Expectation Propagation and Markov chain Monte Carlo methods. The variational inference route taken was a mean field approach to efficiently leverage the mGvM tractable conditionals and create a baseline for comparison with other methods. Then, an Expectation Propagation approach is …
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Bayesian Pharmacokinetic Models for Inference and Optimal Sequential Decision Making with Applications in Personalized Medicine
… more reliable inference by sampling using Hamiltonian Monte Carlo as compared to a standard parameterization. Second, a unified framework for the development and simulation based evaluation of personalization based on pharmacokinetic modelling combined with dynamic treatment regimes. …
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Integral geometry, Hamiltonian dynamics, and Markov Chain Monte Carlo
… to the 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, …
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A Comparison of Bayesian Estimation Techniques in a Multidimensional Two-Parameter Partial Credit Item Response Model
… the performance of two Bayesian 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 …
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Comparative Analysis of Geometric Random Walks for Sampling in High-Dimensional Convex Polytopes
… στο θεωρητικό υπόβαθρο των μεθόδων Markov Chain Monte Carlo (MCMC), εξετάζοντας όχι μόνο τους παραδοσιακούς γεωμετρικούς τυχαίους περιπάτους (όπως Ball Walk, Hit-and-Run, Coordinate-Directions Hit-and-Run και Billiard Walks), αλλά και πιο εξειδικευμένες μεθόδους (Barrier Walks, π.χ. Dikin, …
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Advanced Sampling Methods for Solving Large-Scale Inverse Problems
… The sampling strategy is based on a Hybrid/Hamiltonian Monte Carlo (HMC) approach that can handle non-normal probability distributions. The first algorithm proposed in this work is the "HMC sampling filter", an ensemble-based data assimilation algorithm for solving the sequential filtering …