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 ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

This course is available on the MSc in African Development, MSc in Development Management, MSc in Development Studies, MSc in Health and International Development, MSc in International Development and ...

The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families ...

A nonlinear regression model is applied to several sets of enzyme kinetics data, treating the entire regression vector as the parameter of interest. The resulting marginal posterior distributions are ...

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 ...