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Title: General Bayesian modeling

Abstract: The work is motivated by the inflexibility of Bayesian modeling; in that only parameters of probability models are required to be connected with data. The idea is to generalize this by allowing arbitrary unknowns to be connected with data via loss functions. An updating process is then detailed which can be viewed as arising in at least a couple of ways - one being purely axiomatically driven.聽聽
The further exploration of replacing probability model based approaches to inference with loss functions is ongoing.
Joint work with Chris Holmes, Pier Giovanni Bissiri and Simon Lyddon.