Modern Bayesian Inference in Implicit Probabilistic Models

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Recent advances in generative adversarial networks have sparked tremendous excitement about the more general area of implicit probabilistic models. These models are only defined via simulations from an unknown (implied) distribution and provide a much more flexible data-modelling approach than traditional prescribed probabilistic models. However, their generality comes at the expense of extremely difficult inference challenges.

This project will develop new methods for inference in implicit probabilistic models and is expected to have a significant impact in areas such as evolutionary biology, ecology, high-energy physics, financial models and portfolio optimisation, where the combination of simulators with data-driven approaches is ubiquitous.


The ideal candidate should have completed a degree in a quantitative discipline such as Computer Science, Statistics, Physics, Electronic Engineering or related disciplines. He/she should have a strong Mathematics and Statistics background with excellent programming skills and proficiency in programming languages such as Python, Matlab, R or C++. He/She must be a highly-motivated student, proactive, curious and enthusiastic about scientific research.

Supervisory team

Computer Science and Engineering

Business School

Mathematics & Statistics
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