Understanding Credibility Theory in Actuarial Science

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

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

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, ...
Diginomica

Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...
Visual Studio Magazine

Gaussian Naive Bayes Classification Using the scikit Library

Dr. James McCaffrey of Microsoft Research says the main advantage of using Gaussian naive Bayes classification compared to other techniques like decision trees or neural networks is that you don't ...
Diginomica

How can Bayesien Inference support complex decisions? A practical guide to an overlooked approach

In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, ...
JSTOR Daily

BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES

The Annals of Applied Statistics, Vol. 14, No. 1 (March 2020), pp. 241-256 (16 pages) ABOUZAHR, C., CLELAND, J., COULLARE, F., MACFARLANE, S. B., NOTZON, F. C., SETEL ...