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 12 of 12 for “"Dirichlet Process Mixture Model"”.
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The Cauchy-Net Mixture Model for Clustering with Anomalous Data
… consequences, such as when building prediction models for housing prices. To combat anomalies, we develop the Cauchy-Net 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 …
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Recurrent-Event Models for Change-Points Detection
… develops recurrent-event change-point models to detect the time when driving risk decreases significantly for novice teenager drivers. The dissertation consists of three major parts: the first part applies recurrent-event change-point models with identical change-points for all …
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On fault tolerance of hardware samplers
… particle filtering and clustering using a Dirichlet Process Mixture Model (DPMM). Our results indicate that hardware samplers are indeed robust to hardware faults and that their robustness improves in the context of application level metrics. Specifically, we observed that (a) the two …
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Unsupervised learning of lexical subclasses from phonotactics
… applies a state-of-the-art clustering method (a Dirichlet process mixture model) to a substantial number of Japanese and English words extracted from corpora. It turns out that the predicted clusters largely correspond to the etymologically defined sublexica. Since the clustering method is …
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Learning Probabilistic Generative Models For Fast Sampling-Based Planning
… approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new …
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Probabilistic Programming over Heterogeneous Language and Hardware Targets
… yet existing platforms force users to trade modeling flexibility for performance. This thesis introduces GenUflect, a metalanguage that embeds multiple Gen-compatible dialects inside a single program, allowing each sub-component to run on the most appropriate language and hardware target …
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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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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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Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation
… address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation …
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Pathogen evolution within and between hosts with applications to the pneumococcus and SARS-CoV-2
… genomes is presented. An approximation to a Dirichlet process mixture model is shown to produce accurate genome clusterings 10-100 times faster than existing methods. Next, the problem of identifying the pangenome (the set of all genes that have been found in a species) is considered and a …
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Societal risk and resilience analysis: A multi-scale approach to model the dynamics of infrastructure-social systems
… and adaptable mathematical approach that can model the performance of infrastructure at different scales of space and time, subject to different hazards. For the reliability and serviceability analysis of infrastructure, the dissertation develops general probabilistic and stochastic models. …
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Statistical modeling of heterogeneous data
This dissertation is centered on the modeling of heterogeneous data which is ubiquitous in this digital information age. From the statistical point of view heterogeneous data is composed of dissimilar components, where objects in each component are homogeneous themselves. One such example from the …