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An interactive visual tutorial for Bayesian parameter estimation


Suitable for

MSc in Computer Science
Computer Science, Part B
Mathematics and Computer Science, Part C
Computer Science and Philosophy, Part C
Computer Science, Part C


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.