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 22 for “"variational Bayes"”.
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Advances in Variational Bayes Theory: Adaptation, Uncertainty Quantification, and Amortization
Variational methods and variational inference have been widely used in statistical physics, Bayesian posterior approximation, and modern generative modeling. At a high level, a variational method transforms an often difficult or intractable inference problem into an optimization problem by …
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Variational inference for non-stationary distributions
In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as …
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Disentangling time constant and time dependent hidden state in time series with variational Bayesian inference
… and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constant and time dependent dynamics for use with compatible time series, such as those that can be modelled by …
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Stochastic Modelling and Approximate Bayesian Inference: Applications in Object Tracking and Intent Analysis
As two fundamental pillars of Bayesian inference for time series, stochastic modelling and approximate Bayesian inference play crucial roles in providing accurate priors for underlying random processes and addressing the challenges of evaluating posterior distributions when exact computation is …
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Low dimensional visualization and modelling of data using distance-based models
… with regards to the size of the network, and Variational Bayes, which yields faster but poorer approximations. This work establishes that LSMs can instead be estimated with expectation propagation (EP). Necessary changes to the algorithm and the optimization procedures are shown and the …
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Bayesian generalized additive model selection
… non-linear or zero on the mean response. We use Bayesian model selection paradigms and group least absolute shrinkage and selection operator (LASSO) priors. Two types of priors are explored for the sparse fits. The first, Laplace-Zero and Grouped Lasso-Zero priors, is applied to Gaussian and …
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Bayesian methods in music modelling
… thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is …
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Bayesian Computation for Variable Selection and Multivariate Forecasting in Dynamic Models
… dissertation presents techniques for efficient Bayesian computation in multivariate time series analysis. Computational scalability is a core focus of this work, and often rests on the decouple-recouple concept in which multivariate models are decoupled into univariate models for efficient …
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Use and Development of Matrix Factorisation Techniques in the Field of Brain Imaging
… Factorisation (SMF) model, inferred through Variational Bayes (VB). By modelling the components as a mixture, more general distributions can be expressed. The VB approach scaled to 600 subjects from Cam-CAN, enabling a comparison to, and validation of, the main findings of an earlier …
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Bug vision : experiments in low resolution vision
… well as point tracking strategies are discussed. Bayesian solutions to the point-tracking problem are well understood, because the generative models need describe the dynamics of simple point objects. In addition, the radar tracking problem assumes that measurements are noise corrupted positions, …
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Linear mixed model for multi-level omics data
… traits. In the first project, I developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient …
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Fast and efficient approaches to large-scale occupancy models
Bayesian occupancy models are important statistical tools that are used to investigate species range dynamics, species interactions as well as undercover key biological processes that drive occupancy (and detection) in a particular region. The results from these models are used to answer pressing …
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Statistical methods for learning sparse features
… (PMD). In chapter 4, this thesis considers a Bayesian approach to sparse principal component analysis (PCA). An efficient algorithm, which is based on a hybrid of Expectation-Maximization (EM) and Variational-Bayes (VB), is proposed and it can be shown to achieve selection consistency when …
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Latent class profile analysis : inference, estimation and its applications
… two solutions, reversible jump MCMC and the Bayesian non-parametric approach, so as to provide a set of principles for the systematic model selection for the stage-sequential process. The reversible jump MCMC sampler can explore parameter space and automatically learn the model. Nevertheless, …
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New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection
… genetic variants and red blood cell traits. Bayesian divide and conquer and subsampling methods have been studied in the fixed model setting but little attention has been given to model selection. An important task in Bayesian model selection is computation of the integrated likelihood. We …
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Statistical inference for complex networks
… networks. In the first project, we propose a variational inference algorithm for dynamic latent space models. Compared with sampling-based methods such as Markov chain Monte Carlo (MCMC), the proposed algorithm yields similar empirical performance with much less computation time. We derive the …
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An investigation of heterogeneous commuting mode choices through an Early Stopping Bayesian Data Assimilation approach
… This dissertation develops an Early Stopping Bayesian Data Assimilation (ESBDA) estimator which enables an established behaviour model to be well adapted to a new context characterised by significantly lower data samples. We carry out the development using the Mixed-Logit as the main behaviour …
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Statistical Surrogate Models for Robust Design Optimisation in Reduced Dimension
… with adaptive sampling methodologies such as Bayesian optimisation (BO), solutions that are more robust to an incorrect specification of the linear subspace dimension are proposed; due to the solutions being proposed from the design variable probability density that accurately estimates …
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Improving Clinical Prediction Models with Statistical Representation Learning
… the ideas from representation learning, variational Bayes, causal inference, and contrastive training, this dissertation builds tools for risk modeling frameworks that are robust to various peculiarities of real-world datasets to yield reliable individualized risk evaluations.</p><p>This …
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Statistical models and inference for dynamic networks
… 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 incorporated into the model, obtaining a …
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