Nonstationarity, or changes in the generative parameters, are often a key aspect of real world time series, which comprise of many distinct parameter regimes. An inability to react to regime changes can have a detrimental impact on predictive performance. Change point detection (CPD) attempts to reduce this impact by recognizing regime change events and adapting the predictive model appropriately. As a result, it can be a useful tool in a diverse set of application domains including robotics, process control, and finance. CPD is especially relevant to finance where risk resulting from parameter changes is often neglected in models. For example, Gaussian copula models used in pricing collateralized debt obligations (CDOs) had two key flaws: assuming that subprime mortgage defaults have a fixed correlation structure, and using a point estimate of these correlation parameters learned from historical data prior to the burst of the real-estate bubble [1, 2]. Bayesian change point analysis avoids both of these problems by assuming a change point model of the parameters and integrating out the uncertainty in the parameters rather than using a point estimate.