# Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics

arXiv: https://arxiv.org/abs/1808.02763 · HTML: https://mlsanity.com/paper/1808.02763/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [Deep learning via LSTM models for COVID-19 infection forecasting in India](https://mlsanity.com/paper/2101.11881/) (2021)
- [Bayesian neural networks via MCMC: a Python-based tutorial](https://mlsanity.com/paper/2304.02595/) (2023)
- [Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing](https://mlsanity.com/paper/2301.10876/) (2023)

## Abstract (arXiv)

Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages on geological timescales. We demonstrate the existence of multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent the environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of parameters in pyReef-Core, which provides the groundwork for future research in this area.