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» Estimation of non-stationary Markov Chain transition models
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UAI
2003
13 years 8 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
TIT
2010
111views Education» more  TIT 2010»
13 years 2 months ago
Designing floating codes for expected performance
Floating codes are codes designed to store multiple values in a Write Asymmetric Memory, with applications to flash memory. In this model, a memory consists of a block of n cells, ...
Flavio Chierichetti, Hilary Finucane, Zhenming Liu...
CIKM
2009
Springer
14 years 2 months ago
A social recommendation framework based on multi-scale continuous conditional random fields
This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social recommendation emphasizes utilizing various attributes informatio...
Xin Xin, Irwin King, Hongbo Deng, Michael R. Lyu
COR
2008
116views more  COR 2008»
13 years 7 months ago
Supply disruptions with time-dependent parameters
We consider a firm that faces random demand and receives product from a single supplier who faces random supply. The supplier's availability may be affected by events such as...
Andrew M. Ross, Ying Rong, Lawrence V. Snyder
GLOBECOM
2009
IEEE
14 years 2 months ago
Random Linear Network Coding for Time-Division Duplexing: Field Size Considerations
Abstract— We study the effect of the field size on the performance of random linear network coding for time division duplexing channels proposed in [1]. In particular, we study ...
Daniel Enrique Lucani, Muriel Médard, Milic...