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Invariance Principle for Lifts of Geodesic Random Walks. [PDF]

J Theor Probab
Junné J, Redig F, Versendaal R.
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Comparison of GRNN-MC and RF models for predicting soil hydrogeological and geotechnical profile using borehole data. [PDF]

Sci Rep
Darzi AG, Sadeghi H, Moezzi SMM.
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The Scaling Limit of the Volume of Loop-<i>O</i>(<i>n</i>) Quadrangulations. [PDF]

Commun Math Phys
Aïdékon É, Da Silva W, Hu X.
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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
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Markov random fields and gibbs random fields

Israel Journal of Mathematics, 1973
Spitzer has shown that every Markov random field (MRF) is a Gibbs random field (GRF) and vice versa when (i) both are translation invariant, (ii) the MRF is of first order, and (iii) the GRF is defined by a binary, nearest neighbor potential. In both cases, the field (iv) is defined onZ v, and (v) at anyxeZv, takes on one of two states.
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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.
George R. Cross, Anil K. Jain 0001
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