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BMCBI
2004
208views more  BMCBI 2004»
13 years 10 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
ICML
2006
IEEE
14 years 11 months ago
Graph model selection using maximum likelihood
In recent years, there has been a proliferation of theoretical graph models, e.g., preferential attachment and small-world models, motivated by real-world graphs such as the Inter...
Adam Kalai, Ivona Bezáková, Rahul Sa...
NIPS
2000
13 years 11 months ago
High-temperature Expansions for Learning Models of Nonnegative Data
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
Oliver B. Downs
TSP
2008
179views more  TSP 2008»
13 years 10 months ago
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fie...
V. Chandrasekaran, Jason K. Johnson, Alan S. Wills...
CSDA
2007
82views more  CSDA 2007»
13 years 10 months ago
Non-parametric log-concave mixtures
Finite mixtures of parametric distributions are often used to model data of which it is known or suspected that there are subpopulations. Instead of a parametric model, a penalize...
Paul H. C. Eilers, M. W. Borgdorff