ArticleiScience2023
Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.Royal Society open science · 2024Article
- Inferring fungal growth rates from optical density data.PLoS computational biology · 2024Article
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3 authors.
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Abstract
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter and prediction uncertainties. Yet, generating representative samples from the posterior distribution is often computationally challenging. Here, we present an approach that lowers the computational complexity of sample generation for dynamical models with scaling, offset, and noise parameters. The proposed method is based on the marginalization of the posterior distribution. We provide analytical results for a broad class of problems with conjugate priors and show that the method is suitable for a large number of applications. Subsequently, we demonstrate the benefit of the approach for applications from the field of systems biology. We report an improvement up to 50 times in the effective sample size per unit of time. As the scheme is broadly applicable, it will facilitate Bayesian inference in different research fields.
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Registered trials
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