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Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.
Ariadna Quattoni +4 more
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2006 3rd International Symposium on Voronoi Diagrams in Science and Engineering, 2006
We propose a random field regarded as a generalization of Voronoi diagrams for the case where the positions of generators are distributed probabilistically. Our approach is a kind of stochastic approaches to Voronoi diagrams; however our definition is different from usual random Voronoi diagrams such as Poisson Voronoi diagrams.
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We propose a random field regarded as a generalization of Voronoi diagrams for the case where the positions of generators are distributed probabilistically. Our approach is a kind of stochastic approaches to Voronoi diagrams; however our definition is different from usual random Voronoi diagrams such as Poisson Voronoi diagrams.
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Advances in Applied Probability, 1978
is of great importance in many applications. For example, if we consider a geographical map and denote height by X(t) where t is the set of geographical coordinates, Z(S) is the height of the highest mountain in the area S. In general, it is not possible to make any exact useful statements about the distribution of Z(S), and one must have recourse to ...
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is of great importance in many applications. For example, if we consider a geographical map and denote height by X(t) where t is the set of geographical coordinates, Z(S) is the height of the highest mountain in the area S. In general, it is not possible to make any exact useful statements about the distribution of Z(S), and one must have recourse to ...
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Computer Graphics Forum, 1985
AbstractThis paper presents an algorithm for random fields generation. The main idea of the paper is an improvement of the recursive technique presented by A. Fournier, D. Fussel and L. Carpenter in [4]. In order to ensure the continuity constraints on the boundaries of the cells generated at different stages of the algorithm, we show that it is ...
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AbstractThis paper presents an algorithm for random fields generation. The main idea of the paper is an improvement of the recursive technique presented by A. Fournier, D. Fussel and L. Carpenter in [4]. In order to ensure the continuity constraints on the boundaries of the cells generated at different stages of the algorithm, we show that it is ...
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INERTIAL PARTICLES IN A RANDOM FIELD
Stochastics and Dynamics, 2002The motion of an inertial particle in a Gaussian random field is studied. This is a model for the phenomenon of preferential concentration, whereby inertial particles in a turbulent flow can correlate significantly. Mathematically the motion is described by Newton's second law for a particle on a 2-D torus, with force proportional to the difference ...
Sigurgeirsson, H., Stuart, A. M.
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Structures in random fields: Gaussian fields
Physical Review A, 1992We present two alternative methods for evaluating the probability densities of structures defined by d degrees of freedom in random fields. For Gaussian random fields, both differentiable and nondifferentiable, the application of these methods is considered in detail.
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Random fields on random graphs
Advances in Applied Probability, 1992The distribution (1) used previously by the author to represent polymerisation of several types of unit also prescribes quite general statistics for a random field on a random graph. One has the integral expression (3) for its partition function, but the multiple complex form of the integral makes the nature of the expected ...
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Random Processes and Random Fields
2009Overview: Because the open channel through which we propagate electromagnetic radiation is often considered a turbulent medium, we present a brief review in this chapter of the main ideas associated with a random field, which in general is a function of a vector spatial variable R and time t.
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