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Bayesian approach to decompression sickness model parameter estimation.

Publication ,  Journal Article
Howle, LE; Weber, PW; Nichols, JM
Published in: Computers in biology and medicine
March 2017

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.

Duke Scholars

Published In

Computers in biology and medicine

DOI

EISSN

1879-0534

ISSN

0010-4825

Publication Date

March 2017

Volume

82

Start / End Page

3 / 11

Related Subject Headings

  • Sensitivity and Specificity
  • Risk Factors
  • Risk Assessment
  • Reproducibility of Results
  • Proportional Hazards Models
  • Prognosis
  • Prevalence
  • Oxygen
  • Nitrogen
  • Models, Statistical
 

Citation

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Howle, L. E., Weber, P. W., & Nichols, J. M. (2017). Bayesian approach to decompression sickness model parameter estimation. Computers in Biology and Medicine, 82, 3–11. https://doi.org/10.1016/j.compbiomed.2017.01.006
Howle, L. E., P. W. Weber, and J. M. Nichols. “Bayesian approach to decompression sickness model parameter estimation.Computers in Biology and Medicine 82 (March 2017): 3–11. https://doi.org/10.1016/j.compbiomed.2017.01.006.
Howle LE, Weber PW, Nichols JM. Bayesian approach to decompression sickness model parameter estimation. Computers in biology and medicine. 2017 Mar;82:3–11.
Howle, L. E., et al. “Bayesian approach to decompression sickness model parameter estimation.Computers in Biology and Medicine, vol. 82, Mar. 2017, pp. 3–11. Epmc, doi:10.1016/j.compbiomed.2017.01.006.
Howle LE, Weber PW, Nichols JM. Bayesian approach to decompression sickness model parameter estimation. Computers in biology and medicine. 2017 Mar;82:3–11.
Journal cover image

Published In

Computers in biology and medicine

DOI

EISSN

1879-0534

ISSN

0010-4825

Publication Date

March 2017

Volume

82

Start / End Page

3 / 11

Related Subject Headings

  • Sensitivity and Specificity
  • Risk Factors
  • Risk Assessment
  • Reproducibility of Results
  • Proportional Hazards Models
  • Prognosis
  • Prevalence
  • Oxygen
  • Nitrogen
  • Models, Statistical