AIDA interprets searching for the "most interesting alternative" as an issue of optimal (acoustic) context-aware Bayesian trial design.
We introduce Reactive Message Passing (RMP) as a framework for executing schedule-free, robust and scalable message passing-based inference in a factor graph representation of a probabilistic model.
The AIF literature describes multiple VFE objectives for policy planning that lead to epistemic (information-seeking) behavior.
We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.
This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.