Modeling the biological growth with a random logistic differential equation

Abstract We modeled biological growth using a random differential equation (RDE), where the initial condition is variable, and rate suitable stochastic process. These assumptions let us obtain model that represents well process observed in nature, only few individuals of population reach maximal size species, curve for every individual behaves randomly. Since we assumed assigned priori density, performed Bayesian inference to update condition’s density RDE. The Karhunen–Loeve expansion was then used approximate coefficient Then, RDE’s approximations, estimated f ( p , t ). Finally, fitted this giant electric ray (or Cortez ray) Narcine entemedor . Simulations solution logistic were construct describes solutions’ mean each time. As result, confidence intervals described reasonably data. fit proposed with training dataset, tested different dataset. selection square errors.