  Revision exercises on Probability
 Derive the Moment Generating Function for a Poission distribution.
Use this to show that the sum of n independent Poission variables with means λ_{1}, λ_{2}, ..., λ_{n} is itself a Poisson variable with mean λ_{1}+λ_{2}+...+λ_{n}.
Let S_{n} be the sum of n independent, identically distributed Poission variables each with mean λ, let X_{n} be S_{n}/n and let Z_{n} be (X_{n}λ)/(√λ/√n).
Derive the MGFs for X_{n} and for Z_{n}.
Consider the limit of the MGF for Z_{n} as n becomes large.
How does this relate to the Central Limit Theorem?
 CST 2008 Paper 4 Question 4 (random walk and gambler's ruin)
Hint: The second part of the question was not covered in lectures this year, but is fairly straightforward probability.
Let r_{a} be the probability of ruin starting with a capital of a.
Consider the first time step to express r_{a} in terms of r_{a+1} and r_{a1} for 0<a<N.
Establish boundary conditions for r_{0} and r_{N}.
Solve the resulting second order linear difference equation, bearing in mind the possibility of degenerate special cases.
 Consider a Markov chain with transition matrix P, such that the states constitute a single class.
Relate the nature of these states to the solutions to πP=π.
A prisoner on Devil's Island has probability p_{0} of escaping on any given day;
if he is still free i days after the escape, the he will continue to evade capture for a further day with probability p_{i}.
If recaptured, he immediately resumes his attempts at escape.
Formulate a Markov chain that models the number of days for which he reamins free after each attempt to escape.
Show that 'ultimate escape' (appropriately defined) is almost certain if Πp_{i}>0, and has zero probability otherwise.
Relate these two cases to the character of the process.
 A slot machine works on inserting a penny.
If the player wins, the penny is returned with an additional penny, otherwise the original penny is lost.
The probability of winning is arranged to be ^{1}/_{2} (independently of previous plays) unless the previous play has resulted in a win, in which case the probability is p<^{1}/_{2}.
If the cost of maintaining the machine averages c pence per play, show that the owner must arrange that p<(13c)/2(1c) (with c<^{1}/_{3}) in order to make a profit in the long run.
A player determines to play the machine until he has won r times in succession.
Calculate the expected number of plays required to achieve this.
