An interactive visual tutorial for Bayesian parameter estimation
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Abstract
The aim of this project is to build an educational tool which enables the progress of a Bayesian parameter estimation algorithm
to be visualised. The model to be fitted might be (but is not limited to) a system of Ordinary Differential Equations and
the Bayesian estimation tools might be build around an existing system such as Stan, PyML or Edward. A good tutorial system
should be able to let the user change the underlying model system, introduce noise to a system, visualise interactive updates
to probability distributions, explore the progress of a chosen sampling method such as Metropolis-Hastings and provide enough
information that a novice student can get an intuition into all aspects of the process.