Browsing Theses by Subject "probabilistic graphical models"
Now showing items 1-3 of 3
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Improved Scalability and Accuracy of Bayesian Network Structure Learning in the Score-and-Search Paradigm
(University of Waterloo, 2023-05-16)A Bayesian network is a probabilistic graphical model that consists of a directed acyclic graph (DAG), where each node is a random variable and attached to each node is a conditional probability distribution (CPD). A ... -
New Algorithms for Predicting Conformational Polymorphism and Inferring Direct Couplings for Side Chains of Proteins
(University of Waterloo, 2015-08-20)Protein crystals populate diverse conformational ensembles. Despite much evidence that there is widespread conformational polymorphism in protein side chains, most of the xray crystallography data are modelled by single ... -
Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification
(University of Waterloo, 2019-08-16)Pixel-wise hyperspectral image (HSI) classification has been actively studied since it shares similar characteristics with related computer vision tasks, including image classification, object detection, and semantic ...