Bayesian Identification of Distributed Vector AutoRegressive Processes

In this paper, we address the problem of identification of distributed vector autoregressive (DVAR) processes from partial samples. The DVAR theory builds on the assumption that several processes are evolving in time, and the transition matrices of each process share some common characteristics.

The identification of vector autoregressive (VAR) processes from partial samples is a relevant problem motivated by several applications in finance, econometrics, and networked systems (including social networks). The literature proposes several estimation algorithms, leveraging on the fact that these models can be interpreted as random Markov processes with covariance matrices satisfying Yule-Walker equations.