A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Double-Chain Unscented Expectation Propagation for Inference in Stochastic Dynamical Models of Biological Processes
2013
International Work-Conference on Bioinformatics and Biomedical Engineering
In many fields of biology and medicine we are confronted with the task of analyzing and estimating a complex process based on partial observations. Many of these problems can be described by dynamical system models with unknown states and parameters. The Bayesian theory provides a statistical inference framework for such systems. But there is a fundamental and not satisfactorily solved problem in Bayesian inference of biological processes, that is how to approximate the posterior distributions
dblp:conf/iwbbio/WuB13
fatcat:m3x43kqf6jadbl72lvanl25dh4