Nick jones inference control and driving of natural

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Unformatted text preview: 3 Pick an appropriate choice of priors p (θ|H) given your model. 4 Reason about some actual data and so find the posterior over θ. Nick Jones Inference, Control and Driving of Natural Systems Remark on Priors and Hyperpriors In the literature there is a great deal of discussion about appropriate choice of (un/informatative) priors and debate about the role of confidence intervals etc. [1]. This hasn’t stopped Bayesian inference being very influential and widely industrially applied in Machine Learning. We are going to hope that correctly characterizing the prior information that the biological system has been exposed to will hand us the priors in a natural manner: quantifying the prior experience of a system is part of describing its structure and understanding its function. Prior information and model parameters can have physical interpretations in terms of changes in RAM or disk magnetization or chemical modifications. In practice Bayesians often write their priors p (θ) as parameterized distributions. These parameters are called hyper-parameters e.g. the µ and σ in the prior p (θ) ∼ N (σ, µ). Nick Jones Inference, Control and Driving of Natural Systems Sampling and Posteriors Here is a crude met...
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This document was uploaded on 03/01/2014 for the course EE 208 at Imperial College.

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