Results 271 to 280 of about 256,437 (324)

Trait coevolution and causal inference using generalized dynamic phylogenetic models

Methods in Ecology and Evolution, EarlyView.
Abstract Phylogenetic comparative methods are widely used to study trait coevolution across biological and cultural domains. The most common methods are phylogenetic generalized linear (mixed) models, phylogenetic path analysis, Pagel's ‘discrete’ method and Ornstein–Uhlenbeck models. While some frameworks like generalized linear mixed models are quite
Erik J. Ringen, Scott Claessens, Jordan S. Martin, Adrian V. Jaeggi   +3 more
wiley   +1 more source

Feature Extraction Using Markov Random Field (MRF) On Improving CNN Classification Results For Alzheimer's Disease Diagnosis

Saeful Bahri, Miftah Farid Adiwisastra, Taufik Hidayatulloh   +2 more
openalex   +2 more sources

Epidemic spread with asymptomatic infectious period in contact adaptive networks. [PDF]

Sci Rep
Chai WK, Karaliopoulos M.
europepmc   +1 more source

A Hierarchical Multi-Resolution Self-Supervised Framework for High-Fidelity 3D Face Reconstruction Using Learnable Gabor-Aware Texture Modeling. [PDF]

J Imaging
Mareo P, Fooprateepsiri R.
europepmc   +1 more source

Color Textured Image Segmentation Based on Spatial Dependence Using 3D Co-occurrence Matrices and Markov Random Fields

, 2007
William Robson Schwartz, Hélio Pedrini
openalex   +1 more source

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging. [PDF]

PLoS One
Liu T, Dey J, Xu B, Bridgeford EW, Alldritt S, Nenning KH, Byeon K, Xu T, Vogelstein JT.   +8 more
europepmc   +1 more source

Supervised image segmentation method based on tree-structured Markov random field in wavelet domain

, 2011
Jian Guo Liu
openalex   +1 more source

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Markov Random Field Texture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983
We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models.
Anil K Jain
exaly   +3 more sources

Markov Argumentation Random Fields

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning ...
Yuqing Tang 0001, Nir Oren, Katia P. Sycara   +2 more
openaire   +1 more source