149 search results - page 9 / 30 » Maximum Likelihood Learning of Conditional MTE Distributions |
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
The paper addresses the convergence of a decentralized Robbins-Monro algorithm for networks of agents. This algorithm combines local stochastic approximation steps for finding th...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
UAI
2004
13 years 8 months ago
2004
Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property ...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Discriminative training of graphical models can be expensive if the variables have large cardinality, even if the graphical structure is tractable. In such cases, pseudolikelihood...