A Markov modulated Poisson Process (MMPP) is a Poisson process whose rate varies according to a Markov process. The nonhomogeneous MMPP developed in this article is a natural model for point processes whose events combine irregular bursts of activity with predictable (e.g. daily and hourly) patterns. We show how the MMPP may be viewed as a superposition of unobserved Poisson processes that are ...

A profile hidden Markov model (PHMM) is widely used in assigning protein sequences to protein families. In this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. In other words, in the PHMM, only the information of the left side of a hidden state is considered. However, it makes sense that considering the information of the b...

Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. Within this context, an independent Poisson model has been used for the output distribution of HMMs; hence, the model is incapable of tracking the change in correlation without modulating the firing rate. To achieve thi...

We analyze a periodically inspected system with hidden failures in which the rate of wear is modulated by a continuous-time Markov chain and additional damage is induced by a Poisson shock process. We derive explicitly the system’s lifetime distribution and mean time to failure, as well as the limiting average availability. The main results are illustrated through two numerical examples.

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...