Global ETD Search
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Showing 1 to 12 of 12 for “"hierarchical dirichlet process"”.
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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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Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data
… in a meaningful way. We explore the multi-modal hierarchical Dirichlet process (mmHDP) mixture model as a Bayesian non-parametric approach to data fusion. In particular, we elaborate on its censored-data perspective, which aligns groups of observations at a group level to accommodate for missing …
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Improved robustness and efficiency for automatic visual site monitoring
… by modeling scene-level activities with a Hierarchical Dirichlet Process.
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Nonparametric Methods for Analysis and Modeling of Complex Multivariate Distributions
… compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification jointly in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample flow cytometry …
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Bayesian nonparametric learning of complex dynamical phenomena
… intractable. In some cases, Markov switching processes, with switches between a set of simpler models, are employed to describe the observed dynamics. Such models typically rely on pre-specifying the number of Markov modes. In this thesis, we instead take a Bayesian nonparametric approach in …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
… <p>For sequential data analysis, the dynamic hierarchical Dirichlet process is proposed to capture the temporal dependence across different groups. The data collected at any time point are represented via a mixture associated with an appropriate underlying model; the statistical properties of …
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Bayesian time series models and scalable inference
… 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), and their Bayesian nonparametric extensions. The HMM is ubiquitous in Bayesian time series models, and it and its Bayesian …
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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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Patterns of somatic genome rearrangement in human cancer
… alteration patterns, Chapter 4 introduces the Hierarchical Dirichlet Process as a non-parametric Bayesian model of mutational signatures. After developing methods for consensus signature extraction, I detour to the domain of single nucleotide variants to test the HDP method on real and …
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Modeling Point Patterns, Measurement Error and Abundance for Exploring Species Distributions
… is modeled with a point-level spatial Gaussian process prior, after taking into account sampling bias and change in land-use pattern. The large size of the region enforces using an computational approximation with a bias-corrected predictive process. We compare our methodology against the the …
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Techniques for comparing efficacy and cost-effectiveness of cancer therapies, and improved inference tools
… We perform a network meta-analysis in a hierarchical Bayesian random-effects model to assess the role of immunotherapies and targeted therapies. We also evaluate the impact of immunotherapy biomarkers within a hierarchical Bayesian setting with a view to support and improve the …
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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 …