This 5-day workshop provides an intuitive hands-on introduction to statistical modelling, viewed from the Bayesian perspective. The course starts by covering the very basics of what a model is, building up to fairly sophisticated models by the last session (for example, predicting COVID-19 outcomes from biomarker data). An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers ECTS Equivalent points.

Topics Covered
discrete variables , continuous variables , the concept of a prior , uninformative prior , subjective prior , regularising prior , likelihood , posterior , Bayes' Rule , grid search implementation , Markov Chain Monte Carlo , brms , checking convergence , brms output , interpreting the posterior distribution , Highest Posterior Density Interval (HPDI) , posterior predictive distribution , regression intuition , simulating a regression model in R , posterior predictive checks , biology application , negative binomial distribution , link function , modelling for scientific understanding , medicine application , Bernoulli distribution , modelling for prediction , probability , probability density , binomial distribution , Poisson distribution , normal distribution , history of Bayes , Bayes vs frequentist , advantages of Bayesian methods