Bayes for sequential decision making (II): filtering as learning

Introduction The algorithms we studied in the previous note assumed a stationary environment: the data-generating-process (DGP) does not depend on time. Under this assumption, the learning algorithms gradually converge to a point estimate, which means that they either stop exploring, stop learning, or both. In various real-world scenarios, we do not want our algorithms to converge. The underlying…