Mathematical Modeling
Vol. 8 (2024), Issue 2, pg(s) 44-46

THEORETICAL FOUNDATIONS AND SPECIFICITY OF MATHEMATICAL MODELLING

Key aspects of Markov Chain Monte Carlo simulations in Bayesian statistical analysis

  • 1 Faculty of Computer Science and Engineering, University Ss Cyril and Methodius Skopje, Republic of North Macedonia

Abstract

This paper focuses on the simulation aspects of Bayesian hypothesis testing applied on ophthalmic data. Bayesian statistical analysis often relies on Markov Chain Monte Carlo (MCMC) methods for estimation, when analytical solutions are not possible. We highlight the key aspects of MCMC including model specification, details on the simulation, MCMC diagnostics as well as its limitations and advantages. The simulations are done using R and JAGS.

Keywords

References

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