Associate Professor Sama Low-Choy
Statistical modelling centres on a model, which essentially involves measured (observed) data and unknown parameters. Bayesian statistical models introduce a further layer to modelling, through a "prior" model that encapsulates all that we know about these parameters, prior to considering this dataset. Adding this layer opens up the possibilities for statistical modelling in many different ways.
Each episode: Each session in this series will consider a different reading presenting a case for Bayesian. We invite participants to nominate and vote on articles to be read and presented in each session. Participants may suggest their own article or choose from the list provided.
Format: We will alternate between highlighting talking points from the reading then fielding comments or questions from the audience (with priority given to questions on notice).
Audience: Any level.
Preparation: The session follows a "flipped classroom" approach, that relies on participants to read the nominated reading, prior to attending. They also have the opportunity to submit "questions on notice"