Bayesian approach to decompression sickness model parameter estimation.

Journal Article

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. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.

Full Text

Duke Authors

Cited Authors

  • Howle, LE; Weber, PW; Nichols, JM

Published Date

  • March 2017

Published In

Volume / Issue

  • 82 /

Start / End Page

  • 3 - 11

PubMed ID

  • 28119191

Electronic International Standard Serial Number (EISSN)

  • 1879-0534

International Standard Serial Number (ISSN)

  • 0010-4825

Digital Object Identifier (DOI)

  • 10.1016/j.compbiomed.2017.01.006

Language

  • eng