  Stochastic processes find applications in modelling systems in a variety of
disciplines. A large number of stochastic models considered are Markovian in
nature. It is often observed that higher order Markov processes can model the
data better. However most higher order Markov models are discrete. Here, we
propose a novel continuous-time formulation of higher order Markov processes,
as stochastic differential equations, and propose a method of parameter
estimation by maximum likelihood methods.
