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 148 for “"Bayesian methods."”.
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Bayesian methods for hurdle models.
… coincide. In this dissertation, we explore the Bayesian approach to these models in detail, focusing on prior structures. Many of the Bayesian hurdle models encountered in the literature fail to incorporate expert opinion into the prior structure. We consider how prior information can be …
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Bayesian Methods for Spatial Proteomics
… to alleviating this problem by developing a Bayesian model for spatial proteomics data, with dedicated software. These approaches perform competitively with state-of-the-art classification algorithms whilst Markov-chain Monte Carlo algorithms are employed to sample from the posterior …
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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 Methods for Mineral Processing Operations
… can have a prohibitive computation time. Bayesian statistical methods intrinsically quantify uncertainty of model parameters and predictions given a set of data and a prior distribution and model parameter prior distributions. The uncertainty quantification possible with Bayesian methods …
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Bayesian Methods for Source Separation in Magnetoencephalography
… it is of paramount interest in MEG to develop methods to distinguish between the signal generated by the sources of interest from that which arises from noise sources.We address the source separation problem within the Bayesian framework for both single time slice data and time series data. For …
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Bayesian Methods and Machine Learning in Astrophysics
This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a …
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Bayesian methods in non-clinical pharmaceutical statistics.
… research papers investigating the application of Bayesian methods to pharmaceutical non-clinical statistics. In the first paper, we present an application of Bayesian assurance and sample size determination to the manufacturing process validation life-cycle. In particular, we show how the …
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Scalable sparsity structure learning using Bayesian methods
… and theory. In this thesis we develop scalable Bayesian algorithms based on EM algorithm and variational inference to learn sparsity structure in various models. Estimation consistency and selection consistency of our methods are established. First, a nonparametric Bayes estimator is proposed …
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ESSAYS ON EMPIRICAL ASSET PRICING USING BAYESIAN METHODS
… We develop a simple multivariate extension of a Bayesian variable selection procedure from the statistics literature to estimate posterior probabilities of asset pricing factors using many assets at once. Using a dataset of thousands of individual stocks in the US market, we calculate posterior …
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Non-parametric bayesian methods for structured topic models
… These models take advantage of non-parametric Bayesian techniques (e.g., the two-parameter Poisson-Dirichlet process (PDP)) and Markov chain Monte Carlo methods. Two preliminary contributions of this thesis are 1. The Compound Poisson-Dirichlet process (CPDP): it is an extension of the PDP that …
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Non-parametric bayesian methods for structured topic models
… These models take advantage of non-parametric Bayesian techniques (e.g., the two-parameter Poisson-Dirichlet process (PDP)) and Markov chain Monte Carlo methods. Two preliminary contributions of this thesis are 1. The Compound Poisson-Dirichlet process (CPDP): it is an extension of the PDP that …
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Bayesian methods for gravitational waves and neural networks
… general relativity itself. I apply the tools of Bayesian inference for the examination of gravitational wave data from the LIGO and Virgo detectors. This is used for signal detection and estimation of the source parameters. I quantify the ability of a network of ground-based detectors to localise …
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Nonparametric Bayesian methods for supervised and unsupervised learning
I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it …
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Detecting Brain Effective Connectivity with Supervised and Bayesian Methods
… activity. The main purpose of this work is on methods for studying time series causality. More in details, we focus on a well-establish criterion of causality: the Granger criterion, which is based on the concepts of temporal precedence and predictability. Firstly, we consider the standard …
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Constraining the kinetically dominated Universe: Bayesian methods and primordial cosmology
… parameters, which are then constrained using Bayesian methods and CMB data from the Planck satellite. Chapter 1 places this thesis into the context of the current state of cosmology and introduces the needed theoretical background and notation. The remainder of the thesis presents both my …
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Study and Design of Bayesian Methods for GNSS in Challenging Environments
L'abstract è presente nell'allegato / the abstract is in the attachment
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Bayesian Methods for the Design and Analysis of Cluster Randomised Controlled Trials
… methodological developments in the context of Bayesian approaches to the design and analysis of Cluster Randomised Controlled Trials, which is the focus of this thesis. This thesis begins by identifying and quantifying the practical application of Bayesian methods to such cluster randomised …
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Seismic Data Conditioning and Inversion with Bayesian Methods and Dynamic Time-Warping
Seismic studies are carried out with the aim of delineating resolved images of the subsurface, however as the wave propagates through the earth, energy and resolution diminish. Amplitude attenuates and velocity disperses due to earth’s viscoelasticity resulting in distorted images of the subsurface …
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Computational Bayesian methods applied to complex problems in bio and astro statistics.
In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit …
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