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 37 for “"Bayesian Nonparametric"”.
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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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Bayesian nonparametric reward learning from demonstration
… modifications are proposed to an existing Bayesian IRL algorithm to improve its efficiency and tractability in situations where the state space is large and the demonstrations span only a small portion of it. A modified algorithm is presented and simulation results show substantially faster …
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Bayesian nonparametric learning for complicated text mining
… commercial value. It is commonly accepted that Bayesian models with finite-dimensional probability distributions as building blocks, also known as parametric topic models, are effective tools for text mining. However, one problem in existing parametric topic models is that the hidden topic …
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Active duplicate detection with Bayesian nonparametric models
… technical contributions: a domain-independent Bayesian model expressing the relationship between the unobserved partition and the observed field values of a set of database records; a criterion for picking informative queries based on the mutual information between the response and the …
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Bayesian nonparametric learning with semi-Markovian dynamics
… Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or …
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Planning under uncertainty with Bayesian nonparametric models
… the possibility of multiple models altogether. Bayesian nonparametric (BNP) methods provide the flexibility to solve both of these problems, but have high inference complexity that has limited their adoption. This thesis provides several methods to tractably plan under uncertainty using BNPs. …
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Bayesian nonparametric learning of complex dynamical phenomena
… Markov modes. In this thesis, we instead take a Bayesian nonparametric approach in defining a prior on the model parameters that allows for flexibility in the complexity of the learned model and for development of efficient inference algorithms. We start by considering dynamical phenomena that …
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Genomic variety estimation with Bayesian nonparametric hierarchies
… in the context of genomic projects using a nonparametric Bayesian hierarchical approach, which allows to perform prediction tasks which jointly handle multiple subpopulations at the same time. Moreover, our method performs well on extremely small as well as very large datasets, a desirable …
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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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Sampling in computer vision and Bayesian nonparametric mixtures
… combination of each separate component, which Bayesian formulations handle in a mathematically consistent framework. Unfortunately, probabilistic formulations are often difficult in computer vision due to the complexity and large dimensionality of data. In this thesis, we demonstrate how …
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Asymptotic theory for Bayesian nonparametric procedures in inverse problems
… the frequentist asymptotic properties of nonparametric Bayesian procedures in inverse problems and the Gaussian white noise model. In the first part, we study the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and …
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Bayesian Nonparametric Models and Tests for Association in Survival Data
… survival data. All three topics utilize a nonparametric family of densities that are centered at a parametric family such as the Weibull, normal, or log-logistic, specifically the Polya tree prior and a novel transformed Bernstein polynomial prior. Both priors start from an initial …
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Bayesian nonparametric approaches for reinforcement learning in partially observable domains
… representations of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. We show how the representations learned using Bayesian nonparametric methods result …
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A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition
Nonparametric Bayesian models have become increasingly popular in speech recognition tasks such as language and acoustic modeling due to their ability to discover underlying structure in an iterative manner. These methods do not require a priori assumptions about the structure of the data, such as …
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Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics
Generalized linear mixed models are used to model clustered and longitudinal data in which the distribution of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying …
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Asymptotic theory for Bayesian nonparametric inference in statistical models arising from partial differential equations
… we investigate the theoretical performance of nonparametric Bayesian procedures in such parameter identification problems in PDEs. In particular, inverse regression models for elliptic equations and stochastic diffusion models are considered. In Chapter 2, we study the statistical inverse …
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Disk Diffusion Breakpoint Determination Using a Bayesian Nonparametric Variation of the Errors-in-Variables Model
… rather than the observed test results. Both a Bayesian parametric (2000) and a frequentist nonparametric (2008) procedure have been proposed. However, due to various computational difficulties and an absence of easy to use software for clinicians, neither approach has been adopted for use.</p> …
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Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments
… the flexibility to accommodate new experience. Bayesian nonparametric methods (BNPMs) both allow the complexity of models to be adaptive to data, and provide a principled way for discovering and representing new knowledge.</p><p>In this thesis, we investigate approaches for RL in centralized and …
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Bayesian time series models and scalable inference
… models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference in hierarchical Bayesian time series models based on the hidden Markov model (HMM), hidden semi-Markov model (HSMM), …
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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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