Explicit distributions and time-changed dynamics of the two-state Markov modulated Poisson process

This paper investigates the two-state Markov modulated Poisson process Nₜ N t , also known as the switched Poisson process. Despite its wide range of applications, a comprehensive characterization of the probability law of Nₜ N t has not been fully addressed in the literature. We contribute to filling this gap by deriving explicit closed-form expressions for the probability distribution and by analyzing its asymptotic behavior. These results also establish a rigorous framework for extensions to more complex dynamics. Specifically, we investigate the time-changed switched Poisson process driven by a subordinator, focusing particularly on the Poisson process and on the inverse stable subordinator. For the latter case, we establish a connection with the fractional Poisson process. Our findings bridge Markov modulated Poisson process theory with fractional dynamics.