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UAI
1996
13 years 11 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
TIP
2010
164views more  TIP 2010»
13 years 4 months ago
A Marked Point Process for Modeling Lidar Waveforms
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful ...
Clément Mallet, Florent Lafarge, Michel Rou...
ISQED
2007
IEEE
151views Hardware» more  ISQED 2007»
14 years 4 months ago
Gate Level Statistical Simulation Based on Parameterized Models for Process and Signal Variations
We propose gate level statistical simulation to bridge the gap between the most accurate Monte Carlo SPICE simulation and the most efficient circuit level statistical static timi...
Bao Liu
NIPS
2008
13 years 11 months ago
Modeling the effects of memory on human online sentence processing with particle filters
Language comprehension in humans is significantly constrained by memory, yet rapid, highly incremental, and capable of utilizing a wide range of contextual information to resolve ...
Roger P. Levy, Florencia Reali, Thomas L. Griffith...
FSS
2008
87views more  FSS 2008»
13 years 10 months ago
Representing parametric probabilistic models tainted with imprecision
Numerical possibility theory, belief function have been suggested as useful tools to represent imprecise, vague or incomplete information. They are particularly appropriate in unc...
Cédric Baudrit, Didier Dubois, Nathalie Per...