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 13 of 13 for “"Dirichlet processes"”.
-
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 …
-
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 …
-
Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data
… 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 data in any …
-
Advances in Hierarchical Probabilistic Multimodal Data Fusion
… develop a multimodal extension to hierarchical Dirichlet processes (mmHDPs) where, in contrast to the setting for LDF, we lack observation-level correspondences across modalities and the data arise from an implicit latent variable model. Finally, we develop a novel representation for Dirichlet …
-
Generative modeling of dynamic visual scenes
… model can change over time. While the use of Dirichlet processes (DPs) as priors allows indefinite number of components, incorporating temporal dependencies between DPs remains a nontrivial issue, theoretically and practically. Our research on this problem leads to a new construction of …
-
Computational discovery of gene modules, regulatory networks and expression programs
… GeneProgram, a framework based on Hierarchical Dirichlet Processes, which uses large compendia of mammalian expression data to simultaneously organize genes into overlapping programs and tissues into groups to produce maps of expression programs. I demonstrate that GeneProgram outperforms …
-
Learning motion patterns using hierarchical Bayesian models
… Bayesian models can learn it driven by data with Dirichlet Processes priors.
-
Computational Methods for Investigating Dendritic Cell Biology
… We have developed a method based on Langevin and Dirichlet processes to model and cluster gene expression temporal data, and have used it to identify, on a large scale, genes that present unique and common transcriptional behaviors in response to these two stimuli. Additionally, we have also …
-
Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments
… we study Q learning with Gaussian processes to solve Markov decision processes, and we also employ hierarchical Dirichlet processes as the prior for the control policy parameters to solve partially observable Markov decision processes. For decentralized partially observable Markov …
-
Weighting and moment conditions in Bayesian inference
… to common nonparametric Bayesian models like Dirichlet processes. The scope of our methodology extends beyond our original motivations. In particular, we can tackle a whole class of problems that would ordinarily be handled using estimating equations and robust variance estimation. Such …
-
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 …
-
Probabilistic modelling of somatic alterations in bulk tissue and single cells using repeat DNA
Chromosomal instability characterises several cancer types, in which large-scale structural alterations of the genome accumulate at an increased rate. An important class of structural alterations are somatic copy number alterations (SCNAs). SCNAs have been shown to be major drivers of oncogenesis …