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 111 for “"graphical model"”.
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Bayesian Multilevel-multiclass Graphical Model
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed. One is to learn multiple Gaussian graphical models at multilevel from unknown classes. Another one is to select …
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Changing edges in graphical model algorithms
Graphical models are used to describe the interactions in structures, such as the nodes in decoding circuits, agents in small-world networks, and neurons in our brains. These structures are often not static and can change over time, resulting in removal of edges, extra nodes, or changes in weights …
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A Graphical Model for the Communications Channel
We consider the problem of channel modeling and channel estimation. The widely used wide sense stationary uncorrelated scattering model for the communications channel neglects correlations between different multipath arrivals, but this seems to oversimplify the real channel in many cases. One …
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Interactive graphical model building using virtual reality
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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A novel inference algorithm on graphical model
We present a framework for approximate inference that, given a factor graph and a subset of its variables, produces an approximate marginal distribution over these variables with bounds. The factors of the factor graph are abstracted as as piecewise polynomial functions with lower and upper bounds, …
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Graphical model driven methods in adaptive system identification
… statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that …
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Graphical model driven methods in adaptive system identification
… statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that …
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Massively parallel message passing on a GPU for graphical model inference
Graphical model inference is fundamental to many problems across disciplines. However, its combinatorial nature makes it computationally challenging. For more effective inference, message passing algorithms that expose significant parallelism have been implemented to exploit graphics processing …
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A probabilistic graphical model based data compression architecture for Gaussian sources
… The two aspects of data compression, source modeling, ie. using knowledge about the source, and coding, ie. assigning an output sequence of symbols to each output, are not inherently related, but most existing algorithms mix the two and treat the two as one. This work builds on recent …
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Joint Gaussian Graphical Model for multi-class and multi-level data
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks …
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Theoretical study of two prediction-centric problems : graphical model learning and recommendations
… thesis. PART I. Learning a tree-structured Ising model: We study the problem of learning a tree Ising model from samples such that subsequent predictions based on partial observations are accurate. Virtually all previous work on graphical model learning has focused on recovering the true …
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From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming
… the author has become very familiar with the graphical model and energy minimization point of view in handling computer vision problems - that is, how to combine the local information with the neighborhood interaction information in the graphical system for the inference; and also the author …
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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… The third part considers the star-shape Gaussian graphical model, which is a special case of undirected Gaussian graphical models. It is a multivariate normal distribution where the variables are grouped into one "global" group of variable set and several "local" groups of variable set. When …
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Provable Algorithms for Learning and Variational Inference in Undirected Graphical Models
Graphical models are a general-purpose tool for modeling complex distributions in a way which facilitates probabilistic reasoning, with numerous applications across machine learning and the sciences. This thesis deals with algorithmic and statistical problems of learning a high-dimensional …
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Epitome and its applications
… spatial consideration, the existing epitome model may fail for image recognition and target detection, which directly motivates us to propose the so-called spatialized epitome in this thesis. Extended from the original simple graphical model of epitome, the spatialized epitome provides a …
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Going beyond semantic image segmentation, towards holistic scene understanding, with associative hierarchical random fields
… the Associative Hierarchical Random Field (AHRF) graphical model to take its use beyond that of semantic image segmentation, into object-classes, towards a framework for holistic scene understanding. We provide a working definition for the holistic approach to scene understanding, which allows for …
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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 …
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Learning meaning representations for text generation with deep generative models
… explores conditioning a language generation model with auxiliary variables. By doing so, we hope to be able to better control the output of the language generator. We explore several kinds of auxiliary variables in this thesis, from unstructured continuous, to discrete, to structured discrete …
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Belief propagation generative adversarial networks
… networks (GANs) are a class of generative models based on a minimax game. They have led to significant improvement in the field of unsupervised learning, especially image generation. However, most works in GANs are based on learning the distribution of the input dataset through a …
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Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fields such as computer vision, image processing, and distributed sensor networks. In this thesis we study two central …
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