Bayesian Update For Descriptive Statistics In Fisheries Science

In the present paper we have examined Bayesian update for descriptive statistics for a sample of 730 Por’s Goatfish (Upeneus pori) (Ben-Tuvia and Golani, 1989), collected from Iskenderun Bay, in the northeast Mediterranean Sea. The computational approach uses the Markov Chain Monte Carlo simulation to draw samples from the posterior distributions of model parameters implementing the simulation in OpenBUGS software. We assigned the results of previous studies as a prior distribution. The posterior distribution for mean length and variance were found to be 11.1 cm and 0.003, while for weight, they were 15.7 g and 0.026. The 95% confidence limits of length and weight were 10.99-11.21 and 15.42-16.05 respectively. The key aspect of this research is that when previous studies are included in the estimation, this significantly reduces the variance and uncertainty, leading to a more sufficient and reliable estimation.