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SQUIDS Seminar - Bayesian Computation when privacy matters

Dates:1 November 2023
Times:14:30 - 16:00
What is it:Seminar
Organiser:Department of Mathematics
Who is it for:University staff, External researchers, Current University students
Speaker:Andrew Duncan
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  • In category "Seminar"
  • In group "(Maths) Statistics, quantification of uncertainty, inverse problems and data science"
  • By Department of Mathematics

Schedule:

  • 2:30pm: pretalk
  • 3:05pm: research talk 'Bayesian Computation when privacy matters'

Abstract: When performing Bayesian computation in practice, one is often faced with the challenge that components of the model and/or the data are spread across a network, and cannot be brought together onto a single machine, e.g., due to privacy concerns or sheer volume. I will present some strategies for performing Bayesian computation in this federated setting, including a new approach based on continuous-time Markov processes, which yields a computation -- and communication -- efficient family of Bayesian inference algorithms which enables full federation, while preserving the asymptotic exactness of classical MCMC. We also investigate scenarios where one of the nodes is compromised and quantify the degree of information leakage if communication between the nodes is intercepted.

Speaker

Andrew Duncan

Organisation: Imperial College, London

Travel and Contact Information

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G.113
Alan Turing Building
Manchester

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Jonas Latz

jonas.latz@manchester.ac.uk

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