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UAI
2004
15 years 5 months ago
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...
Peter Hooper
122
Voted
AAAI
1997
15 years 5 months ago
Effective Bayesian Inference for Stochastic Programs
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
Daphne Koller, David A. McAllester, Avi Pfeffer
GECCO
2008
Springer
155views Optimization» more  GECCO 2008»
15 years 4 months ago
Towards memoryless model building
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...
David Iclanzan, Dumitru Dumitrescu
GIS
2008
ACM
15 years 4 months ago
Sparse terrain pyramids
Bintrees based on longest edge bisection and hierarchies of diamonds are popular multiresolution techniques on regularly sampled terrain datasets. In this work, we consider sparse...
Kenneth Weiss, Leila De Floriani
IJCV
2008
146views more  IJCV 2008»
15 years 3 months ago
Scanning Depth of Route Panorama Based on Stationary Blur
This work achieves an efficient acquisition of scenes and their depths along long streets. A camera is mounted on a vehicle moving along a straight or a mildly curved path and a sa...
Jiang Yu Zheng, Min Shi