no code implementations • 26 Jul 2023 • Bert de Vries
The theoretical properties of active inference agents are impressive, but how do we realize effective agents in working hardware and software on edge devices?
no code implementations • 13 Jun 2023 • Magnus Koudahl, Thijs van de Laar, Bert de Vries
Active Inference (AIF) is a corollary of the FEP that specifically details how systems that are able to plan for the future (agents) function by minimising particular free energy functionals that incorporate information seeking components.
1 code implementation • 9 Jun 2023 • Bart van Erp, Wouter W. L. Nuijten, Thijs van de Laar, Bert de Vries
Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages.
1 code implementation • 5 Jun 2023 • Thijs van de Laar, Magnus Koudahl, Bert de Vries
The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment.
1 code implementation • 17 Oct 2022 • Jim Beckers, Bart van Erp, Ziyue Zhao, Kirill Kondrashov, Bert de Vries
Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models.
1 code implementation • 26 Dec 2021 • Albert Podusenko, Bart van Erp, Magnus Koudahl, Bert de Vries
AIDA interprets searching for the "most interesting alternative" as an issue of optimal (acoustic) context-aware Bayesian trial design.
1 code implementation • 25 Dec 2021 • Dmitry Bagaev, Bert de Vries
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.
no code implementations • 1 Sep 2021 • Thijs van de Laar, Magnus Koudahl, Bart van Erp, Bert de Vries
The AIF literature describes multiple VFE objectives for policy planning that lead to epistemic (information-seeking) behavior.
no code implementations • 24 Mar 2021 • Tanya Ignatenko, Kirill Kondrashov, Marco Cox, Bert de Vries
To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning.
no code implementations • L4DC 2020 • Ismail Senoz, Albert Podusenko, Wouter M. Kouw, Bert de Vries
We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.
1 code implementation • 8 Nov 2018 • Marco Cox, Thijs van de Laar, Bert de Vries
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.
no code implementations • 3 Feb 2016 • Thijs van de Laar, Bert de Vries
Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient.