Skip to content
Archive of entries posted by

Statistics is easy! part 2 – can we at least make it look easy?

Well can we at least make it look easy?

For the model as given here, there are two parameters Pc and Pt – but the focus of interest will be on some parameter representing a treatment effect
– Andrew chose Pt – Pc.

But sticking for a while with Pt and Pc – the prior is a surface over Pt and Pc as is the data model (likelihood)

In particular, the prior is a flat surface (independent uniforms)
and the likelihood is Pt^1 (1 – Pt)^29 * Pc^3 (1 – Pc)^7 (the * is from independence)

(If I reversed the treatment and control groups – I should be blinded to that anyways)

Getting confidence into the scaffolding – even if Bayes did or did not intend that.

After noticing an event for my first stats prof I made the mistake of downloading one of his recent papers After suggesting that Bayes might have actually been aiming at getting confidence intervals – the paper suggests “Bayes posterior calculations can appropriately be called quick and dirty” means to obtain confidence intervals. It avoids obvious […]

When experts disagree – plot them along with their uncertainties.

This plot is perhaps an interesting start to pinning down experts (extracting their views and their self assessed uncertainties) – contrasting and comparing them and then providing some kind off overall view. Essentially get experts to express their best estimate and its uncertainty as an interval and then pool these intervals _weighting_ by a pre-test […]

What’s most cool – the question mark in the name or the modelling of zombies?

Some recent interest has been raised by the following publication zombies by an seemingly unknown author – well not quite Smith? I have not had anything to do with predator/prey models since reading Gregory Bateson’s Steps towards an Ecology of Mind – but a question mark in one’s name – that just too cool to […]