University of Cambridge > Talks.cam > Statistics > Kernel Thinning and Stein Thinning

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Kernel Thinning and Stein Thinning

Download to your calendar using vCal

If you have a question about this talk, please contact Qingyuan Zhao .

This talk will introduce two new tools for summarizing a probability distribution more effectively than independent sampling or standard Markov chain Monte Carlo thinning:

1. Given an initial n point summary (for example, from independent sampling or a Markov chain), kernel thinning finds a subset of only square-root n points with comparable worst-case integration error across a reproducing kernel Hilbert space.

2. If the initial summary suffers from biases due to off-target sampling, tempering, or burn-in, Stein thinning simultaneously compresses the summary and improves the accuracy by correcting for these biases.

These tools are especially well-suited for tasks that incur substantial downstream computation costs per summary point like organ and tissue modeling in which each simulation consumes 1000s of CPU hours.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Martin Centre Research Seminar Series - 40th Annual Series of Lunchtime Lectures Senior Parlour, Gonville & Caius College Department of Chemical Engineering & Biotechnology

Other talks

Title to be confirmed Decoding genomic chaos - a pancancer compendium of chromosomal instability CSAR lecture: Decarbonizing road freight (TBC) Geometry and Generalization: The case of Grokking Mergers all the way down: simulating the origin of globular clusters in a galactic context Group Work
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity