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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
DCG
2008
104views more  DCG 2008»
15 years 4 months ago
Finding the Homology of Submanifolds with High Confidence from Random Samples
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
Partha Niyogi, Stephen Smale, Shmuel Weinberger
JMLR
2006
125views more  JMLR 2006»
15 years 4 months ago
Linear Programming Relaxations and Belief Propagation - An Empirical Study
The problem of finding the most probable (MAP) configuration in graphical models comes up in a wide range of applications. In a general graphical model this problem is NP hard, bu...
Chen Yanover, Talya Meltzer, Yair Weiss
CPC
2004
98views more  CPC 2004»
15 years 4 months ago
And/Or Trees Revisited
We consider boolean functions over n variables. Any such function can be represented (and computed) by a complete binary tree with and or or in the internal nodes and a literal in...
Brigitte Chauvin, Philippe Flajolet, Danièl...
MFCS
2010
Springer
15 years 2 months ago
Qualitative Analysis of Partially-Observable Markov Decision Processes
We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with parity objectives. An observationbased strategy relies on partial information...
Krishnendu Chatterjee, Laurent Doyen, Thomas A. He...