Adaptive Variational Particle Filtering in Non-stationary Environments

Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such framework. We formulate an efficient particle filtering methods for non-stationary environment by making connections with the online mirror descent algorithm which is known to be universal online convex optimization algorithm. As a result of this connection, our proposed particle filtering algorithm proves to achieve optimal particle efficiency.