Using Little’s Law in Stochastic Modeling

Stochastic models are used when there is random behavior in the model. These models come with assumptions. In the case of a continuous-time Markov chain, it is assumed that arrivals are sampled from a Poisson distribution and that the waiting times are indepen-dent and exponentially distributed. Making these assumptions allows for ease of computation and simulation. However, what if these as-sumptions are not able to be proved or are not valid? This talk focuses on providing a solution to that problem using patient flow through a hospital as motivation for the theory.