  Thompson Sampling has recently been shown to be optimal in the Bernoulli
Multi-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumes
stationary distributions for the rewards. It is often unrealistic to model the
real world as a stationary distribution. In this paper we derive and evaluate
algorithms using Thompson Sampling for a Switching Multi-Armed Bandit Problem.
We propose a Thompson Sampling strategy equipped with a Bayesian change point
mechanism to tackle this problem. We develop algorithms for a variety of cases
with constant switching rate: when switching occurs all arms change (Global
Switching), switching occurs independently for each arm (Per-Arm Switching),
when the switching rate is known and when it must be inferred from data. This
leads to a family of algorithms we collectively term Change-Point Thompson
Sampling (CTS). We show empirical results of the algorithm in 4 artificial
environments, and 2 derived from real world data; news click-through[Yahoo!,
2011] and foreign exchange data[Dukascopy, 2012], comparing them to some other
bandit algorithms. In real world data CTS is the most effective.
