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 ...

Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both analytically and ...

Bayesian inference is attractive due to its internal coherence and for often having good frequentisi properties. However, eliciting an honest prior may be difficult, and common practice is to take an ...

Here’s our estimate of public support for vouchers, broken down by religion/ethnicity, income, and state: (Click on image to see larger version.) We’re mapping estimates from a hierarchical Bayes ...

Nate Silver, baseball statistician turned political analyst, gained a lot of attention during the 2012 United States elections when he successfully predicted the outcome of the presidential vote in ...

Everyone who spends time with children knows how incredibly much they learn. But how can babies and young children possibly learn so much so quickly? In a recent article in Science, I describe a ...

This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Mathematics with Data Science, BSc in Mathematics with Economics ...