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 168 for “"graphical models"”.
-
Equalization Using Graphical Models
We examine the use of graphical models for the equalization of digital communication channels with memory. Graphical models provide a framework in which the structure of large systems can be exploited to derive efficient estimation algorithms. Furthermore, properties of a graph on which an …
-
Graphical Models for Video Understanding
… for speeding up the naive learning in the graphical models by orders of magnitude. In that sense, I will investigate signal processing techniques, approximate methods, and online learning. I will demonstrate how the theory and algorithms usefully apply to the variety of tasks ranging from …
-
Graphical Models for Video Analysis
U of I Only
-
Approximate inference in graphical models
… the construction of complex hierarchical models for real-world inference tasks. Unfortunately, exact inference in probabilistic models is often computationally expensive or even intractable. A close inspection in such situations often reveals that computational bottlenecks are confined to …
-
Extending expectation propagation for graphical models
Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes …
-
Approximate inference in Gaussian graphical models
… this thesis is approximate inference in Gaussian graphical models. A graphical model is a family of probability distributions in which the structure of interactions among the random variables is captured by a graph. Graphical models have become a powerful tool to describe complex high-dimensional …
-
Scalable Structure Learning of Graphical Models
… learning, a hypothesis-free approach, of graphical models is a field of growing interest due to the power of such models and lack of domain knowledge when applied on complex real-world data. State-of-the-art techniques improve on scalability of structure learning, which is often …
-
High-Dimensional Covariate-Dependent Gaussian Graphical Models
… we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Utilizing a likelihood framework, our methodology jointly estimates all edge and vertex parameters. We further develop …
-
Probabilistic Graphical Models for Crowdsourcing and Turbulence
Graphical models provide a useful framework and formalism from which to modeland solve problems involving random processes. We demonstrate the versatility and usefulness of graphical models on two problems, one involving crowdsourcing and one involving turbulence. In crowdsourcing, we consider the …
-
Structure learning in high-dimensional graphical models
… structure of undirected and directed (causal) graphical models in the high-dimensional setting. Structure learning in graphical models is a central problem in statistics with numerous applications including learning gene regulatory networks from RNA-seq data and learning the dependence …
-
Efficient Multi-Target Tracking using graphical models
… existing MTT algorithms, we use the formalism of graphical models to model the MTT problem according to its probabilistic structure, and subsequently develop e±cient, approximate message passing algorithms to solve the MTT problem. Our modeling approach is able to take into account issues such as …
-
High-dimensional Linear Regression Problems via Graphical Models
… and Lars, the proposed method uses the idea of graphical models and provides unbiased parameter estimates under certain conditions. In addition, the new method provides a detailed graphical conditional correlation structure for the predictors, whereby the real causal relationship between …
-
Hyper and structural Markov laws for graphical models
… on the parameterisation and estimation of graphical models, based on the concept of hyper and meta Markov properties. These state that the parameters should exhibit conditional independencies, similar to those on the sample space. When these properties are satisfied, parameter estimation …
-
Decay of correlations and inference in graphical models
We study the decay of correlations property in graphical models and its implications on efficient algorithms for inference in these models. We consider three specific problems: 1) The List Coloring problem on graphs, [upper case letter g in italic] The MAX-CUT problem on graphs with random edge …
-
Correlation decay and decentralized optimization in graphical models
Many models of optimization, statistics, social organizations and machine learning capture local dependencies by means of a network that describes the interconnections and interactions of different components. However, in most cases, optimization or inference on these models is hard due to the …
-
Approximate inference in graphical models using LP relaxations
Graphical models such as Markov random fields have been successfully applied to a wide variety of fields, from computer vision and natural language processing, to computational biology. Exact probabilistic inference is generally intractable in complex models having many dependencies between the …
-
Graphical models for visual object recognition and tracking
… by articulated or partially occluded objects. Graphical models provide a powerful framework for encoding the statistical structure of visual scenes, and developing corresponding learning and inference algorithms. In this thesis, we describe several models which integrate graphical …
-
Feedback message passing for inference in Gaussian graphical models
For Gaussian graphical models with cycles, loopy belief propagation often performs reasonably well, but its convergence is not guaranteed and the computation of variances is generally incorrect. In this paper, we identify a set of special vertices called a feedback vertex set whose removal results …
-
Predictive genomics in asthma management using probabilistic graphical models
… the creation of a novel method. Probabilistic graphical models (PGMs) are a powerful technique that can overcome limitations of conventional association study approaches.
-
Cutting plane algorithms for variational inference in graphical models
In this thesis, we give a new class of outer bounds on the marginal polytope, and propose a cutting-plane algorithm for efficiently optimizing over these constraints. When combined with a concave upper bound on the entropy, this gives a new variational inference algorithm for probabilistic …
Page 1 of 9