Week(ly quick)ies: Random walks


Since my blog has been experiencing a shortage of content (mostly due to the fact that I am busy with my current research work), I decided to try out a new format that will hopefully motivate me to post more often. “Weekies” i.e. “weekly quickies” are going to be weekly posts of small puzzles that I come up with or come across on the web. I plan to follow up each “weeky” with a longer write-up of solution and discussion roughly within a month or so of the original post. In the meantime do not hesitate to post solutions and discuss the puzzle in the comments under the original post.

Random walks

Consider the K4 graph/tetrahedron depicted below. The following questions concern different random walks starting at the vertex A.

Q1. Assume that at any time step we will move to a random neighboring vertex with the equal probability of 1/3. What’s the probability that we will visit all vertices of the graph before returning to A?

Q2. Given the same walk as in question 1, what’s the probability that we will visit any one vertex out of {B, C, D} at least n times before returning to A?

Q3. Now, let’s change our walk to have a 1/2 probability of remaining at the current vertex and probabilities of 1/6 to move to each of the neighboring vertices. How do answers to questions 1 and 2 change for this walk?

Q4. With the walk described in question 3, what is the expected number of steps we need to take to visit all vertices at least once?

Q5. Let us record the sequence of visited vertices as a string of characters A, B, C, D. Consider a random walk in question 3 and record its first 10 vertices. What’s the probability that the string “ABCD” occurs as a subsequence in the recorded sequence?

Q6. We can generalize the walk described in question 3 using the parameter q ∈ [0, 1] by letting the probability of staying at the current vertex be q and probability of going to any given neighbor be (1-q)/3. Additionally, let the start vertex be picked uniformly at random. Let n be the length of the sequence of vertices that we record, analogously to question 5. Let Pq,n be the distribution on the Ω={A, B, C, D}n generated by the described process. Let Un be the uniform probability distribution on the Ω. Let D be the total variation distance function and let us denote d(q,n) = D(Pq,n, Un). Investigate behavior of the function d : [0, 1] ⨉ ℕ → [0, 1].

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