 Solution

The history $\sigma (S_0,\dots ,S_n)$ is contained in $\sigma (Y_1,\dots ,Y_n)$. The increment $Y_{n+1}$ is independent of this history by the given independence of random variables. ThereforeThis depends only on the current state and not on time. It is precisely the time-homogeneous Markov property, so $S_n$ is a Markov chain with transition probabilitiesThe conditional identity is an identity that holds almost surely; histories of zero probability require no division by their probabilities.