Structure Learning and Mixed Radix representation in Continuous Time Bayesian Networks
(English)Manuscript (preprint) (Other academic)
Continuous time Bayesian Networks (CTBNs) are graphical representations of the dependence structures between continuous time random processes with finite state spaces. We propose a method for learning the structure of the CTBNs using a causality measure based on Kullback-Leibler divergence. We introduce the causality matrix can be seen as a generalized version of the covariance matrix. We give a mixed radix representation of the process that much facilitates the learning and simulation. A new graphical model for tick-by-tick financial data is proposed and estimated. Our approach indicates encouraging results on both the tick-data and on a simulated example.
Continuous time Bayesian networks; Composable Markov Process; Graphical models; Mixed Radix; Integrated Information Theory; Causality; High-frequency data.
Probability Theory and Statistics
Research subject Economics; Applied and Computational Mathematics
IdentifiersURN: urn:nbn:se:kth:diva-193926OAI: oai:DiVA.org:kth-193926DiVA: diva2:1034595
FunderSwedish Research Council, 2009-5834
QC 201610132016-10-122016-10-122016-10-13Bibliographically approved