Hierarchical gaussian filtering of sufficient statistic time series for active inference
| Autor: | Christoph Mathys, Lilian A.E. Weber |
|---|---|
| Přispěvatelé: | Verbelen, Tim, Lanillos, Pablo, Buckley, Christopher L., De Boom, Cedric |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Series (mathematics)
Computer science Gaussian Inference Bayesian inference Gaussian filter Hierarchical Gaussian filter symbols.namesake Exponential family Settore M-PSI/02 - Psicobiologia e Psicologia Fisiologica Message passing symbols Active inference Precision-weighted prediction errors Minification Algorithm Sufficient statistic Exponential families |
| Zdroj: | Mathys, C & Weber, L 2020, Hierarchical gaussian filtering of sufficient statistic time series for active inference . in T Verbelen, P Lanillos, C L Buckley & C De Boom (eds), Active Inference-First International Workshop, IWAI 2020, Co-located with ECML/PKDD 2020, Proceedings . Springer, Communications in Computer and Information Science, vol. 1326, pp. 52-58, 1st International Workshop on Active Inference, IWAI 2020 held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Ghent, Belgium, 14/09/2020 . https://doi.org/10.1007/978-3-030-64919-7_7 Active Inference ISBN: 9783030649180 |
| DOI: | 10.1007/978-3-030-64919-7_7 |
| Popis: | Active inference relies on state-space models to describe the environments that agents sample with their actions. These actions lead to state changes intended to minimize future surprise. We show that surprise minimization relying on Bayesian inference can be achieved by filtering of the sufficient statistic time series of exponential family input distributions, and we propose the hierarchical Gaussian filter (HGF) as an appropriate, efficient, and scalable tool for active inference agents to achieve this. |
| Databáze: | OpenAIRE |
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