Adaptation and Change Detection With a Sequential Monte Carlo Scheme

Given the sequential data from an unknown target system with changing parameters, the first part of this paper discusses online algorithms that adapt to smooth as well as abrupt changes. This paper examines four different parameter/hyperparameter dynamics for online learning and compares their performance within an online Bayesian learning framework. Using the dynamics that performed best in the first part, the second part of this paper attempts to perform change detection in unknown systems in terms of the time dependence of the marginal likelihood. Because of the sequential nature of the algorithms, a sequential Monte Carlo scheme (particle filter) is a natural means for implementation