ArticleInfectious Disease Modelling2020
Bayesian inference for dynamical systems.
Article in Infectious Disease Modelling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bayesian-calibrated global sensitivity analysis for mathematical models using generative AI.PLoS computational biology · 2026Article
- The tale of two assumptions: incorporating healthcare-seeking behaviour in epidemic forecasting.BMC infectious diseases · 2025Article
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
- The decision uncertainty toolkit: Risk measures and visual outputs to support decision making during public health crises.PloS one · 2025Article
- Modelling daily weight variation in honey bee hives.PLoS computational biology · 2023Article
- Article
- Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation.Nonlinear dynamics · 2023Article
- Mathematical modeling of the dynamics of COVID-19 variants of concern: Asymptotic and finite-time perspectives.Infectious Disease Modelling · 2022Article
- Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model.Infectious Disease Modelling · 2022Article
- Scrutinizing the spread of COVID-19 in Madagascar.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2021Article
- Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models.Infectious Disease Modelling · 2021Article
- Why is it difficult to accurately predict the COVID-19 epidemic?Infectious Disease Modelling · 2020Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed methodology. This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian inference and it covers utilizing different distributions, Markov Chain Monte Carlo (MCMC) sampling, obtaining credible intervals for parameters, and prediction intervals for solutions. A logistic growth example is given to illustrate the methodology.
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