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NIPS
2008
15 years 3 days ago
Efficient Sampling for Gaussian Process Inference using Control Variables
Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algori...
Michalis Titsias, Neil D. Lawrence, Magnus Rattray
PERCOM
2009
ACM
15 years 5 months ago
Markov Chain Existence and Hidden Markov Models in Spectrum Sensing
—The primary function of a cognitive radio is to detect idle frequencies or sub-bands, not used by the primary users (PUs), and allocate these frequencies to secondary users. The...
Chittabrata Ghosh, Carlos de M. Cordeiro, Dharma P...
COMPUTING
2006
130views more  COMPUTING 2006»
14 years 10 months ago
Dynamic Data Driven Simulations in Stochastic Environments
To improve the predictions in dynamic data driven simulations (DDDAS) for subsurface problems, we propose the permeability update based on observed measurements. Based on measurem...
Craig C. Douglas, Yalchin Efendiev, Richard E. Ewi...
STOC
1997
ACM
125views Algorithms» more  STOC 1997»
15 years 2 months ago
An Interruptible Algorithm for Perfect Sampling via Markov Chains
For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an...
James Allen Fill
ICCV
2005
IEEE
15 years 4 months ago
A Model-Based Vehicle Segmentation Method for Tracking
Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and tra...
Xuefeng Song, Ramakant Nevatia