An Order Reduction Design Framework for Higher-Order Binary Markov Random Fields [PDF]
Entropy, 2023 The order reduction method is an important approach to optimize higher-order binary Markov random fields (HoMRFs), which are widely used in information theory, machine learning and image analysis.
Zhuo Chen, Hongyu Yang, Yanli Liu
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Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields [PDF]
Frontiers in Neurology, 2019 Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions
Nagesh K. Subbanna +6 more
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MRFalign: protein homology detection through alignment of Markov random fields. [PDF]
PLoS Computational Biology, 2014 Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein ...
Jianzhu Ma +3 more
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Annals of Applied Probability, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stuart Geman, Athanasios Kehagias
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Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution [PDF]
Frontiers in Aging Neuroscience, 2017 18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D2/3 receptors.
Fermín Segovia +5 more
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Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields [PDF]
Sensors, 2018 In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements.
Mahdi Jadaliha +4 more
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Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields. [PDF]
PLoS ONE, 2015 Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers.
Sean Robinson +5 more
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On Functions of Markov Random Fields [PDF]
2020 IEEE Information Theory Workshop (ITW), 2021 7 pages, submitted to IEEE Information Theory ...
Bernhard C. Geiger, Ali Al-Bashabsheh
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Uncertainty Quantification for Markov Random Fields [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2021 We present an information-based uncertainty quantification method for general Markov Random Fields. Markov Random Fields (MRF) are structured, probabilistic graphical models over undirected graphs, and provide a fundamental unifying modeling tool for statistical mechanics, probabilistic machine learning, and artificial intelligence.
Panagiota Birmpa, Markos A. Katsoulakis
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Random Fields in Physics, Biology and Data Science
Frontiers in Physics, 2021 A random field is the representation of the joint probability distribution for a set of random variables. Markov fields, in particular, have a long standing tradition as the theoretical foundation of many applications in statistical physics and ...
Enrique Hernández-Lemus +1 more
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