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Mathematical Modelling meets Uncertainty: Bayesian Statistics - a Primer

Friday, October 21, at 4pm, in A2-002.

Title: Mathematical Modelling meets Uncertainty: Bayesian Statistics - a Primer

Abstract: The Bayesian paradigm is the most satisfying statistical framework from a mathematician's perspective.  In fact, this framework should be called 'Mathematical modelling under uncertainty,' since to a Bayesian it is in the development of the model that most effort should be spent and it should realistically represent the phenomenon of interest. Observational data then allow us to quantify our uncertainty in the parameters of our model. Finally our predictive distribution allows us examine the implications of our modelling taking proper account of the data - and then we have the choice to revise our model if necessary (or indeed run more experiments if needed.) 

With a mixture of measure theory, differential equations and some statistics, this talk seeks to explain what I do everyday to mathematicians from all backgrounds.

If you have any questions regarding this seminar, please direct them to Iain Moyles (061 233726, iain.moyles@ul.ie).

A full list of upcoming seminars can be found at https://ulsites.ul.ie/macsi/node/48011

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