Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models
We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp lib...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/62 |
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| author | Samuel William Nehrer Jonathan Ehrenreich Laursen Conor Heins Karl Friston Christoph Mathys Peter Thestrup Waade |
| author_facet | Samuel William Nehrer Jonathan Ehrenreich Laursen Conor Heins Karl Friston Christoph Mathys Peter Thestrup Waade |
| author_sort | Samuel William Nehrer |
| collection | DOAJ |
| description | We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison. |
| format | Article |
| id | doaj-art-4168291e60994642b3b1621b2c8a3954 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-4168291e60994642b3b1621b2c8a39542025-01-24T13:31:52ZengMDPI AGEntropy1099-43002025-01-012716210.3390/e27010062Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference ModelsSamuel William Nehrer0Jonathan Ehrenreich Laursen1Conor Heins2Karl Friston3Christoph Mathys4Peter Thestrup Waade5School of Culture and Communication, Aarhus University, 8000 Aarhus, DenmarkSchool of Culture and Communication, Aarhus University, 8000 Aarhus, DenmarkDepartment of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78457 Konstanz, GermanyVERSES Research Lab., Los Angeles, CA 90016, USAInteracting Minds Centre, Aarhus University, 8000 Aarhus, DenmarkInteracting Minds Centre, Aarhus University, 8000 Aarhus, DenmarkWe introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison.https://www.mdpi.com/1099-4300/27/1/62active inferencefree energy principlepredictive processingMarkov decision processcognitive modellingJulia |
| spellingShingle | Samuel William Nehrer Jonathan Ehrenreich Laursen Conor Heins Karl Friston Christoph Mathys Peter Thestrup Waade Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models Entropy active inference free energy principle predictive processing Markov decision process cognitive modelling Julia |
| title | Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models |
| title_full | Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models |
| title_fullStr | Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models |
| title_full_unstemmed | Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models |
| title_short | Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models |
| title_sort | introducing tt activeinference jl tt a julia library for simulation and parameter estimation with active inference models |
| topic | active inference free energy principle predictive processing Markov decision process cognitive modelling Julia |
| url | https://www.mdpi.com/1099-4300/27/1/62 |
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