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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 61 for “"Dirichlet Process"”.
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Using Dirichlet Process Priors For Bayesian Mixture Clustering
… that follow multinomial distributions. The Dirichlet Process is applied as the prior distribution. The method estimates the number of populations together with the allele frequencies and the ancestry coefficients of each individual. Distance matrices and bootstrap support numbers based on …
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Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models
… Bayesian approaches to quantile regression usingDirichlet process mixture (DPM) models. All the existing quantile regression methodsbased on DPMs require the kernel density to satisfy the quantile constraint, hence thekernel densities are themselves usually in the form of mixtures. One innovation …
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Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models
Dirichlet process mixture (DPM) has been widely used as exible prior in nonparametric Bayesian literature, and Weighted Dirichlet process mixture (WDPM) can be viewed as extension of DPM which relaxes model distribution assumptions. Meanwhile, WDPM requires to set weight functions and can cause …
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Non-Parametric Priors for Functional Data and Partition Labelling Models
… papers introduced a variety of extensions of the Dirichlet process to the func-</p><p>tional domain, focusing on the challenges presented by extending the stick-breaking</p><p>process. In this thesis some of these are examined in more detail for similarities</p><p>and differences in their …
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Parallel and distributed MCMC inference using Julia
… transformations and a parallel MCMC sampler for Dirichlet Process Mixture Models (DPMM). Instead of parallelizing over multiple cores on a single machine, our Julia implementations extend existing implementations by parallelizing over multiple machines. We compare our implementation with these …
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A Bayesian classification framework with label corrections
… method, such as the kernel method and Dirichlet Process (DP) priors. With a thorough study of the kernel and Dirichlet Process method, we successfully applied our framework onto these non-parametric methods and achieved satisfactory results in simulations. This work shows the power of …
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Nonparametric Methods for Analysis and Modeling of Complex Multivariate Distributions
… concerns hierarchical modeling of weights of a Dirichlet Process Mixture. We build on the Hierarchical Dirichlet Process where an infinite-parameter mean measure is taken as a Dirichlet Process Mixture and child measures are drawn as Dirichlet Process Mixtures with the base distribution taken as …
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Bayesian nonparametric learning with semi-Markovian dynamics
There is much interest in the Hierarchical Dirichlet Process 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 …
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Sampling in computer vision and Bayesian nonparametric mixtures
… of this thesis, we focus on inference in the Dirichlet process mixture model (DPMM), which is often slow and cumbersome due to the infinite number of mixture components. We develop a parallel algorithm that samples from the posterior distribution of a DPMM without requiring finite model …
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Temporally Correlated Dirichlet Processes in Pollution Receptor Modeling
… by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source …
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Recurrent-Event Models for Change-Points Detection
… develops a non-parametric Bayesian model with a Dirichlet process prior. In the first part, two recurrent-event change-point models to detect the time of change in driving risks are developed. The models are based on a non-homogeneous Poisson process with piecewise constant intensity functions. …
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A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition
… and can learn this structure directly. Dirichlet process mixtures (DPMs) are a widely used nonparametric Bayesian method which can be used as priors to determine an optimal number of mixture components and their respective weights in a Gaussian mixture model (GMM). Because DPMs …
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Wideband Spectrum Sensing and Signal Classification for Autonomous Self-Learning Cognitive Radios
… CR architecture is based on a sequence of signal processing and machine learning techniques that enable the Radiobot to sense a wide frequency band and act autonomously by learning from past experience. To achieve its goals, the proposed CR is equipped with the following functionalities: 1) …
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Advanced Nonparametric Bayesian Functional Modeling
… A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so …
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Heterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modeling
… for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining …
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Accelerated clustering through locality-sensitive hashing
… of mixture components is inferred by assuming a Dirichlet process as a generative model. The separation probability of this process, [alpha], is typically a small constant. We speed up each iteration of the EM algorithm from O(nd2k) to O(ndk log 3(k/a))+nd 2 ) time and each iteration of Lloyd's …
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Semiparametric Bayesian Joint Model With Variable Selection
… and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against possible misspecifications and allows the clustering of subjects. A fully Bayesian method for subset …
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Essays on Semi-parametric Bayesian Econometric Methods
… Least Square estimator, which employs the Dirichlet Process prior to cope with potential heterogeneity in the error distributions. Two methods are discussed as special cases of the GLS estimator, the Seemingly Unrelated Regression for equation systems, and the Random Effects Model for panel …
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Non-parametric bayesian methods for structured topic models
… retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to be further explored using more powerful tools. One potentially useful area is to directly consider the document structure ranging from semantically …
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Non-parametric bayesian methods for structured topic models
… retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to be further explored using more powerful tools. One potentially useful area is to directly consider the document structure ranging from semantically …
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