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AAAI
1997
13 years 10 months ago
Effective Bayesian Inference for Stochastic Programs
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
Daphne Koller, David A. McAllester, Avi Pfeffer
BMCBI
2010
118views more  BMCBI 2010»
13 years 9 months ago
Identifying differentially regulated subnetworks from phosphoproteomic data
Background: Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrati...
Martin Klammer, Klaus Godl, Andreas Tebbe, Christo...
PERCOM
2007
ACM
14 years 8 months ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday
ICCAD
1998
IEEE
82views Hardware» more  ICCAD 1998»
14 years 1 months ago
Symbolic model checking of process networks using interval diagram techniques
In this paper, an approach to symbolic model checking of process networks is introduced. It is based on interval decision diagrams (IDDs), a representation of multi-valued functio...
Karsten Strehl, Lothar Thiele
ICML
2010
IEEE
13 years 10 months ago
Continuous-Time Belief Propagation
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allow...
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman