On the analysis of a new Markov chain which has applications in AI and machine learning

Abstract

In this paper, we consider the analysis of a fascinating Random Walk (RW) that contains interleaving random steps and random “jumps”. The characterizing aspect of such a chain is that every step is paired with its counterpart random jump. RWs of this sort have applications in testing of entities, where the entity is never allowed to make more than a pre-specified number of consecutive failures. This paper contains the analysis of the chain, some fascinating limiting properties, and some initial simulation results. The reader will find more detailed results in [12].