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DAGSTUHL
2007
15 years 1 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys
ARC
2007
Springer
152views Hardware» more  ARC 2007»
14 years 11 months ago
Statistical signal processing approaches to fault detection
: The parity space approach to fault detection and isolation (FDI) has been developed during the last twenty years, and the focus here is to describe its application to stochastic ...
Fredrik Gustafsson
POPL
2002
ACM
15 years 12 months ago
Stochastic lambda calculus and monads of probability distributions
Probability distributions are useful for expressing the meanings of probabilistic languages, which support formal modeling of and reasoning about uncertainty. Probability distribu...
Norman Ramsey, Avi Pfeffer
UAI
2008
15 years 1 months ago
Sparse Stochastic Finite-State Controllers for POMDPs
Bounded policy iteration is an approach to solving infinitehorizon POMDPs that represents policies as stochastic finitestate controllers and iteratively improves a controller by a...
Eric A. Hansen
WSC
1998
15 years 28 days ago
Adaptive Stochastic Manpower Scheduling
Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in ...
Elmira Popova, David P. Morton