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FS
2010
95views more  FS 2010»
13 years 6 months ago
Pricing credit derivatives under incomplete information: a nonlinear-filtering approach
This paper considers a general reduced form pricing model for credit derivatives where default intensities are driven by some factor process X. The process X is not directly observ...
Rüdiger Frey, Wolfgang Runggaldier
JMLR
2010
202views more  JMLR 2010»
13 years 2 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
IPSN
2004
Springer
14 years 1 months ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
CONCUR
1992
Springer
13 years 11 months ago
On the Semantics of Petri Nets
Petri Place/Transition (PT) nets are one of the most widely used models of concurrency. However, they still lack, in our view, a satisfactory semantics: on the one hand the "...
José Meseguer, Ugo Montanari, Vladimiro Sas...
CORR
2010
Springer
160views Education» more  CORR 2010»
13 years 7 months ago
Scalable Probabilistic Databases with Factor Graphs and MCMC
Incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice modeling power, scalability, or treatment of relatio...
Michael L. Wick, Andrew McCallum, Gerome Miklau