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» Learning Nonlinear Dynamic Models from Non-sequenced Data
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UAI
2003
13 years 9 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
JORS
2010
189views more  JORS 2010»
13 years 2 months ago
Monte Carlo scenario generation for retail loan portfolios
Monte Carlo simulation is a common method for studying the volatility of market traded instruments. It is less employed in retail lending, because of the inherent nonlinearities in...
J. L. Breeden, D. Ingram
ATMOS
2011
261views Optimization» more  ATMOS 2011»
12 years 7 months ago
On the Utilisation of Fuzzy Rule-Based Systems for Taxi Time Estimations at Airports
The primary objective of this paper is to introduce Fuzzy Rule-Based Systems (FRBSs) as a relatively new technology into airport transportation research, with a special emphasis o...
Jun Chen, Stefan Ravizza, Jason A. D. Atkin, Paul ...
NETWORKING
2004
13 years 9 months ago
On Detection of Anomalous Routing Dynamics in BGP
BGP, the de facto inter-domain routing protocol, is the core component of current Internet infrastructure. BGP traffic deserves thorough exploration, since abnormal BGP routing dy...
Ke Zhang, Amy Yen, Xiaoliang Zhao, Daniel Massey, ...
CVPR
2011
IEEE
13 years 3 months ago
Online Detection of Unusual Events in Videos via Dynamic Sparse Coding
Real-time unusual event detection in video stream has been a difficult challenge due to the lack of sufficient training information, volatility of the definitions for both norm...
Bin Zhao, Li Fei-Fei, Eric Xing