Welcome to the repository of tutorials on how to do Bayesian Statistics using Julia and Turing. Tutorials are available at storopoli.io/Bayesian-Julia. Bayesian ...

Pilot trials have a key role in preparing for definitive randomized trials, yet determining their sample size remains a common challenge. This article provides practical guidance, methods, and ...

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...

Some machine learning models belong to either the “generative” or “discriminative” model categories. Yet what is the difference between these two categories of models? What does it mean for a model to ...

Bayesian Statistics for Beginners. A Step-by-Step Approach (Donovan and Mickey, 2019) is, perhaps, the “truest-to-title” book I have read on Bayesian inference and statistics, insofar (a) it is ...

ABSTRACT: A probabilistic formalism, relying on Bayes’ theorem and linear Gaussian inversion, is adapted, so that a monochromatic problem can be investigated. The formalism enables an objective test ...

Your browser does not support the audio element. Machine learning is part art and part science. When you look at machine learning algorithms, there is no one solution ...

Bayesian analysis is firmly grounded in the science of probability and has been increasingly supplementing or replacing traditional approaches based on P values. In this review, we present gradually ...