Bayesian Inference in Epidemic Modelling: A Beginner's Guide
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911521637924864 |
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| author | Okolie, Augustine |
| author_facet | Okolie, Augustine |
| contents | This lecture note provides a self-contained introduction to Bayesian inference and Markov Chain Monte Carlo (MCMC) methods for parameter estimation in epidemic models. Using the classical Susceptible-Infectious-Recovered (SIR) compartmental model as a running example, we derive the likelihood function from first principles, specify priors on the transmission and recovery parameters, and implement the Metropolis-Hastings algorithm to sample from the posterior distribution. The note is aimed at graduate students and researchers in mathematical epidemiology with limited prior exposure to Bayesian statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15175 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bayesian Inference in Epidemic Modelling: A Beginner's Guide Okolie, Augustine Methodology Dynamical Systems Populations and Evolution This lecture note provides a self-contained introduction to Bayesian inference and Markov Chain Monte Carlo (MCMC) methods for parameter estimation in epidemic models. Using the classical Susceptible-Infectious-Recovered (SIR) compartmental model as a running example, we derive the likelihood function from first principles, specify priors on the transmission and recovery parameters, and implement the Metropolis-Hastings algorithm to sample from the posterior distribution. The note is aimed at graduate students and researchers in mathematical epidemiology with limited prior exposure to Bayesian statistics. |
| title | Bayesian Inference in Epidemic Modelling: A Beginner's Guide |
| topic | Methodology Dynamical Systems Populations and Evolution |
| url | https://arxiv.org/abs/2603.15175 |