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ICML
2008
IEEE
15 years 10 months ago
Fast Gaussian process methods for point process intensity estimation
Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attrac...
John P. Cunningham, Krishna V. Shenoy, Maneesh Sah...
ICASSP
2009
IEEE
15 years 4 months ago
Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks
We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial ...
Ondrej Hlinka, Franz Hlawatsch
NIPS
2004
14 years 11 months ago
Using the Equivalent Kernel to Understand Gaussian Process Regression
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...
Peter Sollich, Christopher K. I. Williams
ICML
2003
IEEE
15 years 10 months ago
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
Yaakov Engel, Shie Mannor, Ron Meir
ICASSP
2009
IEEE
15 years 4 months ago
Structured variational methods for distributed inference in wireless ad hoc and sensor networks
Abstract –In this paper, a variational message passing framework is proposed for Markov random fields, which is computationally more efficient and admits wider applicability comp...
Yanbing Zhang, Huaiyu Dai