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DMSN
2008
ACM
13 years 12 months ago
Probabilistic processing of interval-valued sensor data
When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two ...
Sander Evers, Maarten M. Fokkinga, Peter M. G. Ape...
ISCAS
2007
IEEE
106views Hardware» more  ISCAS 2007»
14 years 4 months ago
Ensemble Dependent Matrix Methodology for Probabilistic-Based Fault-tolerant Nanoscale Circuit Design
—Two probabilistic-based models, namely the Ensemble-Dependent Matrix model [1][3] and the Markov Random Field model [2], have been proposed to deal with faults in nanoscale syst...
Huifei Rao, Jie Chen, Changhong Yu, Woon Tiong Ang...
CIA
2006
Springer
14 years 1 months ago
Learning to Negotiate Optimally in Non-stationary Environments
Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
Vidya Narayanan, Nicholas R. Jennings
CVPR
2007
IEEE
15 years 2 days ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...
AAAI
1994
13 years 11 months ago
Acting Optimally in Partially Observable Stochastic Domains
In this paper, we describe the partially observable Markov decision process pomdp approach to nding optimal or near-optimal control strategies for partially observable stochastic ...
Anthony R. Cassandra, Leslie Pack Kaelbling, Micha...