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 ...

Bayesian methods have emerged as a pivotal framework in the design and analysis of clinical trials, offering a systematic approach for updating evidence as new data become available. By utilising ...

Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

The main focus of this short course will be the Bayesian aspect of it. That means this is a slightly more advanced course requiring some knowledge of basic probability, regression methods, and the R ...

Symmetries in nature make things beautiful; symmetries in data make data handling efficient. However, the complexity of identifying such patterns in data has always bedeviled researchers. Scientists ...

The application of Bayesian methods to large-scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov ...

What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better decisions.

In recent years, something unexpected has been happening in artificial intelligence. Modern AI appears to be breaking a rule that statisticians have preached for nearly a century: Keep models in a ...

The Rev. Thomas Bayes was, as the honorific the Rev. suggests, a clergyman. Too bad he wasn’t a lawyer. Maybe if he had been, lawyers today wouldn’t be so reluctant to enlist his mathematical insights ...