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 “"Bayesian nonparametrics"”.
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Truncated Bayesian nonparametrics
… to likewise increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allows the number of expressed traits to both be random and to grow with the dataset size. We also require …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
… with multiple images, a hierarchical Bayesian model called H-LSBP is proposed. By sharing the same mixture atoms for different images, the model infers the inter-similarity between each pair of images, and hence can be employed for image sorting.</p>
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Improved prediction and optimal sequencing strategies for genomic variant discovery via Bayesian nonparametrics
… mentioned above. My approach relies on a Bayesian nonparametric formulation that facilitates (i) prediction for the number of new variants in the follow-up study based on the pilot study. I show empirically that, when experimental conditions are kept constant between the pilot and …
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Multiagent planning with Bayesian nonparametric asymptotics
… about the environment in which a system acts. Bayesian nonparametrics, on the other hand, possess structural flexibility beyond the capabilities of past parametric techniques commonly used in planning systems. This extra flexibility comes at the cost of increased computational cost, which has …
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Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… conventional approaches of statistical modeling. Bayesian nonparametrics constitute a promising research direction, in that such techniques can fit the data with a model that can grow with complexity to match the data. In this dissertation we consider nonparametric Bayesian modeling with …
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Toward Faster Methods in Bayesian Unsupervised Learning
… words, documents, and latent topics, one can use Bayesian probabilistic models. The application of Bayesian unsupervised learning faces three computational challenges. Firstly, existing works aim to speed up Bayesian inference via parallelism, but these methods struggle in Bayesian unsupervised …
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Inference of Low-Dimensional Latent Structure in High-Dimensional Data
… of images, dynamic data, and documents with Bayesian nonparametrics. The thesis consists of three parts.</p><p>First, nonparametric Bayesian methods are considered for recovery of imagery based upon compressive measurements. A truncated beta-Bernoulli process is employed to infer an …
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Bayesian Applications in Financial Econometrics
This thesis consists of three chapters in Bayesian financial econometrics. The three chapters apply both Bayesian nonparametric and parametric methods to financial market and macroeconomic time series. Chapter 1 extends popular discrete time short-rate models to include Markov switching of infinite …
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The Cauchy-Net Mixture Model for Clustering with Anomalous Data
… Mixture Model (CNMM). The CNMM is a flexible Bayesian nonparametric tool that employs a mixture between a Dirichlet Process Mixture Model (DPMM) and a Cauchy distributed component, which we call the Cauchy-Net (CN). Each portion of the model offers benefits, as the DPMM eliminates the …
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Bayesian Nonparametric Modeling and Theory for Complex Data
… and methodological problems associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite dimensional object' can refer to a density, a conditional density, a regression surface or even a manifold. Although Bayesian density …
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Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables
… thesis develops flexible non- and semiparametric Bayesian models for mixed continuous, ordered and unordered categorical data. These methods have a range of possible applications; the applications considered in this thesis are drawn primarily from the social sciences, where multivariate, …
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Contributions to asymptotic theory in nonparametric statistics
… to have a compositional structure. Taking a Bayesian approach, we consider a deep Gaussian process (DGP) prior, which is designed to leverage the compositional structure of the parameter in order to achieve fast convergence rates. We show that the DGP prior does indeed consistently solve the …
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Bayesian Time Series Learning with Gaussian Processes
… present a number of algorithms designed to learn Bayesian nonparametric models of time series. The goal of these kinds of models is twofold. First, they aim at making predictions which quantify the uncertainty due to limitations in the quantity and the quality of the data. Second, they are …
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Probabilistic modelling of somatic alterations in bulk tissue and single cells using repeat DNA
Chromosomal instability characterises several cancer types, in which large-scale structural alterations of the genome accumulate at an increased rate. An important class of structural alterations are somatic copy number alterations (SCNAs). SCNAs have been shown to be major drivers of oncogenesis …