[Turing-Southampton] S3RI seminar: Robert Gramacy, Thursday 15 October 2-3pm

[Sam Collins]

Dear all,

Just a reminder of the seminar today.

To kick off the S3RI seminar series for the 2020/21 academic year, we have Prof Robert Gramacy (Virginia Polytechnic Institute and State University) speaking at the seminar this afternoon at 2 pm, on

"Replication or Exploration? Sequential Design for Stochastic Simulation Experiments".





The talk will be on Microsoft teams, and if you haven't joined yet, but would like to join the S3RI team, please use the link below:

https://teams.microsoft.com/l/team/19%3a71f0ed99e3ce45219eef73b9cbd1b0f3%40thread.tacv2/conversations?groupId=260706f2-7819-4bfe-920a-3d55825d8e9c&tenantId=4a5378f9-29f4-4d3e-be89-669d03ada9d8



Hope to see you there!


Kind regards,

Jessie

________________________________
From: Chieh-Hsi Wu <C-H.Wu at soton.ac.uk>
Sent: 12 October 2020 16:02
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Subject: S3RI seminar: Robert Gramacy, Thursday 15 October 2-3pm


Dear all,

Hope all is well.


This Thursday (15 October) at 2 pm, we have a virtual S3RI seminar from Prof Robert Gramacy (Virginia Polytechnic Institute and State University) on

"Replication or Exploration? Sequential Design for Stochastic Simulation Experiments".



Just a reminder, if you haven't joined yet, but would like to join the S3RI team on Microsoft Teams please use the link below:

https://teams.microsoft.com/l/team/19%3a71f0ed99e3ce45219eef73b9cbd1b0f3%40thread.tacv2/conversations?groupId=260706f2-7819-4bfe-920a-3d55825d8e9c&tenantId=4a5378f9-29f4-4d3e-be89-669d03ada9d8



Kind regards,

Jessie


Title: Replication or Exploration? Sequential Design for Stochastic Simulation Experiments

Robert Gramacy, Virginia Polytechnic Institute and State University

We investigate the merits of replication, and provide methods that search for optimal designs (including replicates), in the context of noisy computer simulation experiments. We first show that replication offers the potential to be beneficial from both design and computational perspectives, in the context of Gaussian process surrogate modeling. We then develop a lookahead based sequential design scheme that can determine if a new run should be at an existing input location (i.e., replicate) or at a new one (explore). When paired with a newly developed heteroskedastic Gaussian process model, our dynamic design scheme facilitates learning of signal and noise relationships which can vary throughout the input space. We show that it does so efficiently, on both computational and statistical grounds. In addition to illustrative synthetic examples, we demonstrate performance on two challenging real-data simulation experiments, from inventory management and epidemiology.

For the current schedule of S3RI seminars, see https://tinyurl.com/s3riseminar<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftinyurl.com%2Fs3riseminar&data=01%7C01%7C%7C0cd28e5bd8c54f6e2b3e08d870e4fc22%7C4a5378f929f44d3ebe89669d03ada9d8%7C0&sdata=6enh4NHEAmXA83XXqvNLMznjdAPNZkfONRDKPTBnPS8%3D&reserved=0>

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