Shaun McKinlay

About

Shaun McKinlay’s research centers on the probabilistic analysis of Markov chains, with a particular focus on their stationary distributions and boundary behavior. His most cited work, “On explicit form of the stationary distributions for a class of bounded Markov chains” (2016, 8 citations), introduces a novel class of discrete-time Markov chains confined to the unit interval [0, 1]. In this framework, the direction of each transition is chosen probabilistically based on the current state, while the jump length is drawn independently. McKinlay’s key contribution lies in deriving explicit, closed-form expressions for the stationary distributions of these chains, a significant advance over typical asymptotic or numerical methods. This work provides a powerful analytical tool for understanding long-term behavior in bounded stochastic processes, with implications for fields like population genetics, queueing theory, and financial modeling. While his citation count reflects a focused, emerging impact, the clarity and mathematical elegance of his results have established him as a promising voice in applied probability. His approach bridges rigorous theory with practical tractability, offering students and researchers a clear pathway into the dynamics of constrained random walks.

Research Focus

Key Achievements

Top Papers

1. 1
   On explicit form of the stationary distributions for a class of bounded Markov chains

   8 citations · 2016

Key Collaborators

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