Bayesian-network

In many domains where there are considerable amount of factors influencing the target variable, the dimension of the parameter space for probabilistic models is exponential in the number of variables, which would require significant amount of training samples to guarantee a reasonable prediction accuracy. For this project, we proposed a way to incorporate the domain knowledge on the independence of causal influence and qualitative constraints which greatly improves the prediction performance by reducing the dimension of feature space as well as constraining the searching space.

In most realistic domains, the variables transit between its possible states over time. The data is generated by the dynamic processes with multiple observations at different time points. Dynamic models are needed for modeling such transition intensities over time.