328 search results - page 51 / 66 » On Approximating Multidimensional Probability Distributions ... |
UAI
2003
13 years 10 months ago
2003
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
FOCS
2004
IEEE
14 years 11 days ago
2004
IEEE
Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of) the input is specified in terms of a probability distribution. We consider the...
PERCOM
2008
ACM
14 years 8 months ago
2008
ACM
Abstract--Analytical models to evaluate and predict "precision" performance of indoor positioning systems based on location fingerprinting are lacking. Such models can be...
SODA
2001
ACM
13 years 10 months ago
2001
ACM
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
APPROX
2005
Springer
14 years 2 months ago
2005
Springer
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...