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NIPS
2008
15 years 9 months ago
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
Identification and comparison of nonlinear dynamical system models using noisy and sparse experimental data is a vital task in many fields, however current methods are computation...
Ben Calderhead, Mark Girolami, Neil D. Lawrence
NIPS
2007
15 years 9 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
AAAI
1998
15 years 9 months ago
Structured Representation of Complex Stochastic Systems
This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
Nir Friedman, Daphne Koller, Avi Pfeffer
AAAI
1996
15 years 9 months ago
Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
Craig Boutilier, David Poole
IJCAI
1989
15 years 8 months ago
A Planning/Scheduling Methodology for the Constrained Resource Problem
This paper presents a new planning/scheduling methodology for the constrained resource problem (CRP), in which the amount of available resources is limited and usually monotonical...
Naiping Keng, David Y. Y. Yun
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