Learning Temporal Qualitative Probabilistic Networks from Data

Abstract

Temporal Qualitative Probabilistic Networks (TQPN) have become a standard tool for modeling various qualitative and temporal causal phenomena. In this paper, we address the issue of TQPN learning from time series data. The structure of TQPN can be constructed by learning Dynamic Bayesian Networks (DBN) based on Markov Chain Monte Carlo (MCMC) method. Specifically, since the causal relationships between variables always follow the time flow, we only consider the causal relationships existing between adjacent time slices. Furthermore, we learn the corresponding relationships of both qualitative influences and qualitative synergies with the conditional probability orderings, and represent the conditional probabilities with the frequency formats. Experiment results illuminate that the method is promising.