Bayesian Approach to Decompression Sickness Model Parameter Estimation

We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator.

Source: Computers in Biology and Medicine - January 16, 2017 Category: Bioinformatics Authors: L.E. Howle, P.W. Weber, J.M. Nichols Source Type: research