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The role of mechanistic models in Bayesian inference

The role of mechanistic models in Bayesian inference

This video was recorded at Bayesian Research Kitchen Workshop (BARK), Grasmere 2008. I'll outline the role of mechanistic models, or simulators, in defining priors in a Bayesian inference setting. In particular I will focus on two main cases: 1) where process based understanding of the system allows us to construct a stochastic simulator for the system - which translates to inference in stochastic processes; 2) where an existing (typically) deterministic mechanistic model exists - which we can then emulate and treat 'correctly' in a Bayesian manner. I will pay special attention to the relation between the simulator and reality, since it is reality that typically is sampled to generate the observations used for inference in the model. I will outline ideas from emulation, and show the challenges I think remain to be solved. This is joint work with lots of people: Alexis Boukouvalas, Yuan Shen, Michael Vrettas, Manfred Opper and many others in the MUCM project.

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