Schedule Info

The Most Likely Path

Daniel Reich



Abstract: In this talk, we present a stochastic shortest path problem that we refer to as the Most Likely Path Problem. On a fairly general class of networks, i.e., series-parallel networks, we will show that lower and upper bounds for the probability of the Most Likely Path (MLP) can be computed efficiently. We will then present a dynamic programming algorithm for identifying the MLP on series-parallel networks.