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Showing 1 to 20 of 21 for “"Dirichlet process mixture"”.
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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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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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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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The Cauchy-Net Mixture Model for Clustering with Anomalous Data
… 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 component, which we call the Cauchy-Net (CN). Each portion of the model offers …
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Recurrent-Event Models for Change-Points Detection
… among drivers by a hierarchical Bayesian finite mixture model; the third part 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 …
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Nonparametric Methods for Analysis and Modeling of Complex Multivariate Distributions
… flow cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; the hierarchical skew normal kernels allow flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the ``coarsening'' strategy makes inference …
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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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Linkage Based Dirichlet Processes
… (MCMC) algorithms for large parameter spaces. Dirichlet Process Mixture Models (DPMMs) have become a Bayesian mainstay for modeling heterogeneous structures, namely clusters, especially when the quantity of clusters is not known with the established MCMC methods. As opposed to many ad-hoc …
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Learning Probabilistic Generative Models For Fast Sampling-Based Planning
… configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex …
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Probabilistic Programming over Heterogeneous Language and Hardware Targets
… 105 points. In a collapsed-Gibbs sampler for a Dirichlet-process mixture model, GenUflect’s elastic allocation (@amortize≤) lets vectorized GPU kernels adapt to a growing number of clusters; the same inference that takes over an hour in Gen executes in seconds with GenUflect. A probabilistic …
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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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Variational Mixture Models for non-Gaussian observations: Applications to molecular data
… Specifically, we apply variational non-Gaussian Dirichlet Process mixture models because they have infinite number of components that allow model-determination and are flexible to model any discrete or continuous data type. We also employ Variational Inference with the “annealing” extension that …
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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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Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables
… is a novel nonparametric hierarchical mixture model for continuous, ordered and unordered categorical data. The model includes a hierarchical prior used to couple component indices of two separate models, which are also linked by local multivariate regressions. This structure …
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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
… of general nonlinear dynamical system, called a Dirichlet Process Mixture Model. The proposed approach uses the observational data from a limited number of simulations and the information available a priori to simplify and solve the nonlinear stochastic differential equations that govern the …
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Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data
… gene expression. We propose a log Gaussian cox process with wavelet-based functional model to quantify the relationship between TF binding site locations and gene expression levels. Through the simulation study, we demonstrate that our method performs well, especially with large sample size and …
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