Paul W. Weber

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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(More)
When radiolabeled precursors and autoradiography are used to investigate turnover of protein components in photoreceptive cone outer segments (COSs), the labeled components--primarily visual pigment molecules (opsins)--are diffusely distributed along the COS. To further assess this COS labeling pattern, we derive a simplified mass-transfer model for(More)
To investigate the nature and mechanisms of decompression sickness (DCS), we developed a system for evaluating the success of decompression models in predicting DCS probability from empirical data. Model parameters were estimated using maximum likelihood techniques. Exact integrals of risk functions and tissue kinetics transition times were derived.(More)
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