Diffusion filtration with approximate Bayesian computation

Abstract

Distributed filtration of state-space models with sensor networks assumes knowledge of a model of the data-generating process. However, this assumption is often violated in practice, as the conditions vary from node to node and are usually only partially known. In addition, the model may generally be too complicated, computationally demanding or even completely intractable. In this contribution, we propose a distributed filtration framework based on the novel approximate Bayesian computation (ABC) methods, which is able to overcome these issues. In particular, we focus on filtration in diffusion networks, where neighboring nodes share their observations and posterior distributions.