Imperfect Data in an Uncertain World

Bayesian analysis and modeling, in which uncertainties are quantified in terms of probability, offers an alternative approach to understanding in meteorological applications. The underlying principle, practiced in fields like archaeology and geology, is the accumulation of evidence. The approach provides a mathematical rule to update existing beliefs in light of new evidence. It requires the data be neither uniform in precision nor the evidence complete. Expressing research results in Bayesian terms makes them simpler to understand and makes inconclusive results less prone to misinterpretation. This note shows that a Bayesian analysis produces an arguably more precise estimate of the long-term U.S. hurricane rate.