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SAC
2008
ACM
13 years 3 months ago
Bayesian inference for a discretely observed stochastic kinetic model
The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observ...
Richard J. Boys, Darren J. Wilkinson, Thomas B. L....
CSDA
2008
122views more  CSDA 2008»
13 years 3 months ago
Bayesian inference for nonlinear multivariate diffusion models observed with error
Diffusion processes governed by stochastic differential equations (SDEs) are a well established tool for modelling continuous time data from a wide range of areas. Consequently, t...
Andrew Golightly, Darren J. Wilkinson
JCB
2006
185views more  JCB 2006»
13 years 3 months ago
Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Andrew Golightly, Darren J. Wilkinson
SMA
2010
ACM
181views Solid Modeling» more  SMA 2010»
12 years 10 months ago
Threshold selection in jump-discriminant filter for discretely observed jump processes
Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is ...
Yasutaka Shimizu
IJON
2010
138views more  IJON 2010»
13 years 2 months ago
A dynamic Bayesian network to represent discrete duration models
Originally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in var...
Roland Donat, Philippe Leray, Laurent Bouillaut, P...