Bayesian factor analysis offers a probabilistic framework for uncovering latent structure in datasets where the number of observed variables greatly exceeds the sample size. By positing that ...

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 ...

The "replication crisis" refers to a problem in the sciences where findings from previous experiments don't hold up when studies are repeated. It is a particular issue for those in the behavioral ...

In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

The FDA’s new draft guidance on Bayesian methods in clinical trials has been hailed by some as a breakthrough that could speed drug development. But statisticians and researchers are divided on ...

Randomized controlled trials are considered the golden standard for estimating treatment effect but are costly to perform and not always possible. Observational data, although readily available, is ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...