Learning probabilistic models of biological systems using active inference with belief propagation

In this work, the normative framework of active inference is integrated with belief propagation for inverting a probabilistic causal model using data generated from planned interactions between a Bayesian modeling agent and a biological system. Thompson sampling of parameter distributions is used to...

Descripción completa

Detalles Bibliográficos
Autor principal: Martínez, Ernesto C.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
Active inference
Bayesian inference
Probabil-istic modeling
Biological systems
Reinforcement learning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/140157
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-140157
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Active inference
Bayesian inference
Probabil-istic modeling
Biological systems
Reinforcement learning
spellingShingle Ciencias Informáticas
Active inference
Bayesian inference
Probabil-istic modeling
Biological systems
Reinforcement learning
Martínez, Ernesto C.
Learning probabilistic models of biological systems using active inference with belief propagation
topic_facet Ciencias Informáticas
Active inference
Bayesian inference
Probabil-istic modeling
Biological systems
Reinforcement learning
description In this work, the normative framework of active inference is integrated with belief propagation for inverting a probabilistic causal model using data generated from planned interactions between a Bayesian modeling agent and a biological system. Thompson sampling of parameter distributions is used to estimate the free energy of the expected future when beliefs about beliefs are rolled over a planning horizon. Learning a probabilistic model for maximizing biomass production in the well-known Baker’s yeast example is used as an ex-ample. The prior parameter distributions in the system model of a fed-batch cultivation are updated as new observations are obtained. Planned action sequences aim to excite the yeast metabolism by introducing changes in the feed rate of two nutrients (glucose and nitrogen). Results obtained demonstrate that by maximizing the model evidence, the proposed approach constraints biological system dynamics to relevant trajectories for improved parametric precision in the preferred region of physiological states that favor biomass productivity.
format Objeto de conferencia
Objeto de conferencia
author Martínez, Ernesto C.
author_facet Martínez, Ernesto C.
author_sort Martínez, Ernesto C.
title Learning probabilistic models of biological systems using active inference with belief propagation
title_short Learning probabilistic models of biological systems using active inference with belief propagation
title_full Learning probabilistic models of biological systems using active inference with belief propagation
title_fullStr Learning probabilistic models of biological systems using active inference with belief propagation
title_full_unstemmed Learning probabilistic models of biological systems using active inference with belief propagation
title_sort learning probabilistic models of biological systems using active inference with belief propagation
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/140157
work_keys_str_mv AT martinezernestoc learningprobabilisticmodelsofbiologicalsystemsusingactiveinferencewithbeliefpropagation
bdutipo_str Repositorios
_version_ 1764820458331439105

Ejemplares similares