We present an approach to reconstructing chemical reaction networks from time series measurements of the concentrations of the molecules involved. Our solution strategy combines t...
A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the histor...
We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a tim...
It is hard to manage the software design documents within a distributed development team. The issues include the format, distribution and evolution of data. This paper mainly focu...
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...