Solving inverse problem of Markov chain with partial observations

The Markov chain is a convenient tool to represent the dynamics of complex sys-tems such as traffic and social systems, where probabilistic transition takes place between internal states. A Markov chain is characterized by initial-state proba-bilities and a state-transition probability matrix. In the traditional setting, a major goal is to study properties of a Markov chain when those probabilities are known. This paper tackles an inverse version of the problem: we find those probabilities from partial observations at a limited number of states. The observations include the frequency of visiting a state and the rate of reaching a state from another. Prac-tical examples of this task include traffic monitoring systems in cities, where we need to infer the traffic volume on single link on a road network from a limited number of observation points. We formulate this task as a regularized optimiza-tion problem, which is efficiently solved using the notion of natural gradient. Us-ing synthetic and real-world data sets including city traffic monitoring data, we demonstrate the effectiveness of our method.