Exponential Growth Model and Stochastic Population Models: A Comparison via Population Data


  • Nurul Ashikin Mohamad Radzi Universiti Teknologi Malaysia
  • Haliza Abd Rahman Faculty of Science, Universiti Teknologi Malaysia
  • Shariffah Suhaila Syed Jamaludin Universiti Teknologi Malaysia
  • Arifah Bahar Universiti Teknologi Malaysia




Population dynamics, exponential model, discrete-time Markov chain, continuous-time Markov chain, Stochastic Differential Equations


A population dynamic model explains the changes of a population in the near future, given its current status and the environmental conditions that the population is exposed to. In modelling a population dynamic, deterministic model and stochastic models are used to describe and predict the observed population. For modelling population size deterministic model may provide sufficient biological understanding about the system, but if the population numbers do become small, then a stochastic model is necessary with certain conditions. In this study, both types of models such exponential, discrete-time Markov chain (DTMC), continuous-time Markov chain (CTMC) and stochastic differential equation (SDE) are applied to goat population data. Results from the simulations of stochastic realisations as well as deterministic counterparts are shown and tested by root mean square error (RMSE). The SDE model gives the smallest RMSE value which indicate the best model in fitting the data.

Author Biography

Haliza Abd Rahman, Faculty of Science, Universiti Teknologi Malaysia

Mathematical Sciences Department


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