We propose a simple estimator based on composite likelihoods for parameter learning in random field models. The estimator can be applied to all discrete graphical models such as Markov random fields and conditional random fields, including ones with higher-order energies. It is computationally efficient because it requires only inference over treestructured subgraphs of the original graph, and ...

I study the zero-temperature phase transition between superfluid and insulating ground states of the Bose-Hubbard model in a random chemical potential and at large integer average number of particles per site. Duality transformation maps the pure Bose-Hubbard model onto the sine-Gordon theory in one dimension (1D), and onto the three dimensional Higgs electrodynamics in two dimensions (2D). In ...

Unambiguous hierarchies [NR93, LR94, NR98] are defined similarly to the polynomial hierarchy; however, all witnesses must be unique. These hierarchies have subtle differences in the mode of using oracles. We consider a “loose” unambiguous hierarchy prUH• with relaxed definition of oracle access to promise problems. Namely, we allow to make queries that miss the promise set; however, the oracle ...

Removing noise from original image is still a challenging problem for researchers. There have been several published algorithm and each approach has its assumptions, advantages and disadvantages. Markov Random Field is ndimensional random process defined on a on a discrete lattice. Markov Random Field is a new branch of probability theory that promises to be important both in theory and applica...

Removing noise from original image is still a challenging problem for researchers. There have been several published algorithm and each approach has its assumptions, advantages and disadvantages. Markov Random Field is n-dimensional random process defined on a on a discrete lattice. Markov Random Field is a new branch of probability theory that promises to be important both in theory and applic...

Detecting hedges and their scope in natural language text is very important for information inference. In this paper, we present a system based on a cascade method for the CoNLL-2010 shared task. The system composes of two components: one for detecting hedges and another one for detecting their scope. For detecting hedges, we build a cascade subsystem. Firstly, a conditional random field (CRF) ...

GEOSTATISTICAL APPLICATIONS Abstract: The inverse problem of determining the spatial dependence of random fields from an experimental sample is a central issue in Geostatistics. We propose a computationally efficient approach based on Spartan Gibbs random fields. Their probability density function is determined by a small set of parameters, which can be estimated by enforcing sample-based const...