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Extending probabilistic programming systems and applying them to real-world simulators

Abstract:

Probabilistic programming is a paradigm that enables us to efficiently write probabilistic models as program code that we can sample, infer underlying parameters and predict outcomes based on complete or incomplete observations. Naturally, stochastic simulators, a special sub-class of simulators containing random variables, internal inference procedures, and the simulation of observations, are structurally rich probabilistic models. However, most simulators are not written in the probabili...

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Division:
MPLS
Department:
Engineering Science
Role:
Author

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Supervisor
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Supervisor
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Supervisor
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Supervisor
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Name:
Engineering and Physical Sciences Research Council
Funder identifier:
http://dx.doi.org/10.13039/501100000266
Grant:
EP/L015897/1
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Name:
Center for Doctoral Training in Autonomous Intelligent Machines and Systems
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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