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» Explaining inferences in Bayesian networks
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NIPS
2004
15 years 3 months ago
Probabilistic Computation in Spiking Populations
As animals interact with their environments, they must constantly update estimates about their states. Bayesian models combine prior probabilities, a dynamical model and sensory e...
Richard S. Zemel, Quentin J. M. Huys, Rama Nataraj...
117
Voted
NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 6 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
113
Voted
ETFA
2006
IEEE
15 years 8 months ago
Advance in Multiply Sectioned Bayesian Networks: Sensor Network Practitioners' Perspective
Multiplysectioned Bayesian networks provide a probabilistic framework for reasoning about uncertain domains in cooperative multiagent systems. Several advances have been made in r...
Y. Xiang, K. Zhang
ICMLA
2008
15 years 3 months ago
A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation
Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Ba...
Silvia Chiappa
FLAIRS
2008
15 years 4 months ago
ThomCat: A Bayesian Blackboard Model of Hierarchical Temporal Perception
We present a Bayesian blackboard system for temporal perception, applied to a minidomain task in musical scene analysis. It is similar to the classic Copycat architecture (Hofstad...
Charles W. Fox