Emily Winn -- Markov chains and their applications to stochastic processes in the discrete time case Generally excellent job on the paper and the presentation! A few specific comments: 1) p. 3 -- "The goal is to determine what the weather in Oz will be based on today’s weather, be it rainy, nice, or snowy." I think it's more accurate to say this is a model that is based on the probabilities. It's not that you're trying to "determine" what the weather will be the next day; it's that you are modeling the probabilities given the weather the previous day. 2) Your proof of Theorem 1 is fine, but a nicer (more "elegant") way to do that would be to use a proof by mathematical induction :) 3) p. 7 -- Stray partial sentence after the statement of Theorem 2. 4) p. 7. The discussion of the Strong Markov Property is a bit incomplete because you have not really explained what a stopping time is. You say a stopping time is "the random instant determined from the observation of a Markov chain and thus the state can be taken without knowledge of future states." But to make sense of the proof, I think you really need to say more about the formal definition: a stopping time is another random variable $T : \Omega \to {\bf N}$ with the property that the event $T = n$ depends only on $X_0, \ldots, X_n$. 5) p. 12. A comment: The proof of the Limit Theorem is very similar to the idea of the "power method" for determining the dominant eigenvalue of a matrix as studied in numerical analysis. 6) p. 15. Instead of saying the Wright-Fisher model says individuals can mate sexually and asexually, I think it might be better just to say that the Wright-Fisher model doesn't incorporate a gender at all for individuals and all pairs can "mate". 7) p. 18. "Markov chains support the idea of quarantine" -- that's true, but virtually any epidemic model does the same (e.g. deterministic ODE models show that quarantine is effective too). Final Project Grade Computation Bibliography: 10/10 Paper: 58/60 Presentation: 30/30 Total: 98/100