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This monograph will be included in a future ACGIH® Signature Publication that will be entitled: Air Sampling Technologies: Principles and Applications. This monograph offers 12 examples of IH calculations that inform professional judgment. Bayes’ theorem quantifies continuous improvement and learning. It says the likelihood of an inference is the product of its prior likelihood and the likelihood of new data, given that inference. Intuitive stopping criteria are illustrated for compliance and workplace monitoring. Both parameter estimation and model selection examples contrast Bayesian with Maximum Likelihood techniques. Parameters are estimated for: calibration curves, compliance decisions, air sample probability distributions and Bayesian Decision Analysis (BDA). Examples select “best” models from polynomials for calibration curves and probability distributions for air sample data.
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