Principal speaker
Associate Professor Sama Low-Choy
Gain experience in developing then using a prior model for effect sizes, to complement the data model, in a Bayesian regression.
Many data analytic procedures, from classical statistics or machine learning, rely on analysis of a single dataset. Reproducibility concerns have encouraged researchers to aim for very large data. However, researchers may at times wish to explicitly build on previous information, by analysing small-moderate datasets that are well-targeted to cover gaps in previous knowledge. Here we describe how a Bayesian statistical framework can be set up to transparently include information from previous studies, and in this way, accumulate knowledge. For instance, when your work is pioneering or time-constrained, you may wish to start with what the experts say initially, then update this with whatever limited data is available. Alternatively, you may wish to see how new data modifies previous findings summarised as a meta-analysis. Another common option is to plead ignorance a priori and adopt a non-informative prior. Bayesian prior models codify what is known a priori, that is, before the new data has been analysed. So, at its simplest, a prior model may reflect plausible bounds on effect sizes, or even a complete lack of prior knowledge.
This session introduces you to Bayesian inference, which focuses on how the data has changed estimates of model parameters (including effect sizes). This contrasts with a more traditional statistical focus on "significance" (how likely the data are when there is no effect) or on accepting/rejecting a null hypothesis (that an effect size is exactly zero). It also contrasts with a machine learning approach, which focuses on predictive performance.
We continue to work with the examples from Part 1, by walking through the process of incorporating priors (already constructed) within a Bayesian analysis. Different options for prior models are supported by various packages; we show examples in JASP and R. Prior sensitivity analysis is covered as a mandatory component of Bayesian analysis, to show how prior choices affect posterior findings.
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RSVP
RSVP on or before Monday 11 September 2023 10.43 am, by email red@griffith.edu.au , or via https://events.griffith.edu.au/d/wpq1hg/