Feynman-Kac path integration models arise in a variety of scientific disciplines, including in computational physics, biology, information theory and computer sciences. Their interpretations are dependent on the application domain. For instance, if we choose the indicator function G n ( x n ) = 1 A ( x n ) of some subset of the state space, they represent the conditional distribution of a Markov chain given it stays in a given tube; that is, we have: