Event Details

Occupational exposure measurements are often sparse, variable, and affected by censoring, yet hygienists are expected to make clear and defensible decisions from them. This session will show how Bayesian statistics can improve both the reliability of exposure decisions and the way uncertainty is communicated to workers, managers, and other stakeholders.

The webinar will introduce the practical interpretation of exposure variability, explain why uncertainty is unavoidable even when measurements are available, and show how Bayesian methods help quantify that uncertainty in a way that is directly useful for decision-making.

Participants should attend if they want a more intuitive, transparent, and decision-oriented way to interpret exposure data.

Presenter

Jérôme Lavoué is a professor of environmental and occupational health at the Université de Montréal. Initially trained as an industrial chemist in France, he later shifted toward public health, specializing in toxicology and risk analysis. His research focuses on exposure assessment, exposure databases, and the development of practical decision-support tools, with an emphasis on Bayesian statistical approaches. He led the development of Expostats, IHSTAT_Bayes, and the Webexpo open-source library for Bayesian analysis of exposure data.

Attendees will receive 0.2 CM Points for attending this event