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ICCV
2011
IEEE
12 years 7 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
DAC
1994
ACM
13 years 11 months ago
Probabilistic Analysis of Large Finite State Machines
Regarding nite state machines as Markov chains facilitates the application of probabilistic methods to very large logic synthesis and formal veri cation problems. Recently, we ha...
Gary D. Hachtel, Enrico Macii, Abelardo Pardo, Fab...
BMCBI
2004
208views more  BMCBI 2004»
13 years 7 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
AAAI
2000
13 years 8 months ago
Multivariate Clustering by Dynamics
We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a tim...
Marco Ramoni, Paola Sebastiani, Paul R. Cohen
CONCUR
2000
Springer
13 years 11 months ago
Weak Bisimulation for Probabilistic Systems
Abstract. In this paper, we introduce weak bisimulation in the framework of Labeled Concurrent Markov Chains, that is, probabilistic transition systems which exhibit both probabili...
Anna Philippou, Insup Lee, Oleg Sokolsky