  Thompson Sampling algorithm is a well known Bayesian algorithm for solving
stochastic multi-armed bandit. At each time step the algorithm chooses each arm
with probability proportional to it being the current best arm. We modify the
strategy by introducing a paramter h which alters the importance of the
probability of an arm being the current best arm. We show that the optimality
of Thompson sampling is robust to this perturbation within a range of parameter
values for two arm bandits.
