|Abstract||This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. However, the intensity function of the proposed process is a mixture of intensities which its complexity grows with the complexity of temporal patterns of data. Moreover, it utilizes a hierarchical dependent nonparametric model for modeling marks of events. These two capabilities allow the model to adapt its complexity according to the complexity of data and be a good candidate for real world scenarios.
The model is nonparametric in two ways. First, it uses a hierarchical nonparametric model for modeling the content of events. Second, it allows the complexity of intensity function of point processes to be adapted based on the complexity of temporal data. An online inference algorithm is proposed that makes the framework applicable to a vast range of applications. The framework is applied to a real world application, modeling the diffusion of contents over networks. Extensive experiments reveal the effectiveness of the proposed framework in comparison to state-of-the-art methods. |