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.
Results
Showing 1 to 20 of 27 for “"nonparametric Bayesian"”.
-
Nonparametric Bayesian behavior modeling
… To overcome these obstacles, this thesis takes a Bayesian approach and applies a Dirichlet process (DP) prior over behaviors, which uses experience to reduce the likelihood of over-fitting or under-fitting the model complexity. Additionally, the DP maintains a probability mass associated with a …
-
Advanced Nonparametric Bayesian Functional Modeling
… and relations can be easily modeled by a Bayesian hierarchical model, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric …
-
Nonparametric Bayesian Modelling in Machine Learning
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced …
-
Nonparametric Bayesian methods for supervised and unsupervised learning
I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it …
-
Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models
We propose new nonparametric 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 …
-
Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… conventional approaches of statistical modeling. Bayesian nonparametrics constitute a promising research direction, in that such techniques can fit the data with a model that can grow with complexity to match the data. In this dissertation we consider nonparametric Bayesian modeling with …
-
Consistency of nonparametric Bayesian methods for two statistical inverse problems arising from partial differential equations
… such statistical inverse problems is through Bayesian methodology. This thesis investigates the theoretical performance of the Bayesian approach in two particular cases. The first model considered is the advection-diffusion equation. Kolmogorov’s equations link this partial differential …
-
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 …
-
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 …
-
Some methods and models for analyzing time-series gene expression data
… include new clustering techniques based on nonparametric Bayesian procedures, and a confirmatory methodology to validate that the clusters produced by any of these methods have statistically different mean paths.
-
Discovering linguistic structures in speech : models and applications
… acoustic signals. In particular, we explore a nonparametric Bayesian framework for automatically acquiring a phone-like inventory of a language. In addition, we integrate our phone discovery model with adaptor grammars, a nonparametric Bayesian extension of probabilistic context-free grammars, …
-
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 …
-
Essays on Group Heterogeneity in Panel Data Models
… in Panel Data Models, we develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to the same group or different groups. We propose a prior to incorporate the …
-
Computability, inference and modeling in probabilistic programming
… and noise, both of which are common in Bayesian hierarchical modeling. This theoretical work bears on the development of probabilistic programming languages (which enable the specification of complex probabilistic models) and their implementations (which can be used to perform Bayesian …
-
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 …
-
Inference of Low-Dimensional Latent Structure in High-Dimensional Data
… of images, dynamic data, and documents with Bayesian nonparametrics. The thesis consists of three parts.</p><p>First, nonparametric Bayesian methods are considered for recovery of imagery based upon compressive measurements. A truncated beta-Bernoulli process is employed to infer an …
-
Natively probabilistic computation
… Markov chain Monte Carlo, and solve difficult Bayesian inference problems. I first introduce Church, a probabilistic programming language for describing probabilistic generative processes that induce distributions, which generalizes Lisp, a language for describing deterministic procedures that …
-
Learning motion patterns using hierarchical Bayesian models
… and large scale data sets using hierarchical Bayesian models. We explore their applications to activity analysis in far-field visual surveillance and tractography segmentation in medical imaging. Many existing activity analysis approaches in visual surveillance are ad hoc, relying on …
-
Bayesian modelling and sampling strategies for ordering and clustering problems with a focus on next-generation sequencing data
… of longitudinal information. I developed a new, Bayesian, way of reconstructing this information computationally, sampling orders efficiently using MCMC on a space of permutations. This Bayesian approach provides novel insights into biological phenomena and experimental artefacts. The second part …
-
Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies
… modeling is a combination of the parametric and nonparametric models in which some functions follow a known form and some others follow an unknown form. In this dissertation we made contributions to semiparametric modeling for matched case-crossover data. In matched case-crossover studies, it is …
Page 1 of 2