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 statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

The U.S. Food and Drug Administration (FDA) has issued new draft guidance modernizing statistical methodologies used in clinical trials, formally recognizing Bayesian approaches that align with the ...

We consider the usual proportional hazards model in the case where the baseline hazard, the covariate link, and the covariate coefficients are all unknown. Both the baseline hazard and the covariate ...

This is a preview. Log in through your library . Abstract Recent developments of high-throughput genomic technologies offer an unprecedented detailed view of the genetic variation in various human ...

The integration of Bayesian statistics into modern analytics has redefined industries, and Alexandre Andorra’s work exemplifies its transformative potential. With expertise spanning sports analytics, ...