A Novel Latent Variable Approach to Bayesian Inference for Hawkes Processes

Abstract

This study is dedicated to exploring the latent variable approach for Bayesian inference in the context of parameter estimation for Hawkes processes. By incorporating latent variables within the Bayesian framework, the research aims to refine the process of parameter inference. Hawkes processes, frequently employed for modeling self-exciting events, stand to benefit from this approach, potentially enhancing the effectiveness of parameter estimation. The study contributes valuable insights into the application of Bayesian methods for improved parameter inference in the domain of temporal point process modeling.

Arya Karami

Researcher of Machine Learning

My research interests include Statistical Network Modeling, Stochastic Processes, Bayesian Inference, Neural Networks.