Learn to apply Bayes' theorem in financial forecasting for insightful, updated predictions. Enhance decision-making with ...

Researchers at Cambridge University made a big splash in April last year when they announced that they had strong evidence ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

Discover how credibility theory helps actuaries use historical data to estimate risks and set insurance premiums; learn how ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

How modern mathematics helps policymakers and insurers connect the dots of the country’s fragmented demographic data ...

Incrementality testing in Google Ads is suddenly within reach for far more advertisers than before. Google has lowered the barriers to running these tests, making lift measurement possible even ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...