Approaches for statistical inference -- The Bayes approach -- Bayesian computation -- Model criticism and selection -- The empirical Bayes approach -- Bayesian design -- Special methods and models -- ...

In the Big Data era, many scientific and engineering domains are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to ...

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

A frequently used experimental design is one in which the experimental units are measured twice (e. g., under different test conditions). When the response variable is dichotomous, the equality of the ...

This is a preview. Log in through your library . Abstract A two-stage Bayesian method is presented for analyzing case-control studies in which a binary variable is ...

Scientists have turned to advanced AI to decode the intricate ecosystem of gut bacteria and their chemical signals. Using a Bayesian neural network called VBayesMM, researchers can now identify ...