Perfect estimation

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• Mark Huber (Claremont McKenna College)
• Friday 29 November 2024, 09:15-10:05
• Seminar Room 1, Newton Institute.

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SSDW04 - Monte Carlo sampling: beyond the diffusive regime

Given the ability to draw Monte Carlo data exactly from a target distribution, the next question is best how to make estimates based on the data.  By drawing a random number of samples, it is often possible to create more useful estimates.  In this talk I’ll discuss methods for estimating the probability p of an event where the distribution of the relative error is unknown, recent improvements to this method that yield a 1 – p factor in the number of samples needed, and how to automatically deweight outliers to get upper bounds on error tails when the coefficient of variation is bounded.

This talk is part of the Isaac Newton Institute Seminar Series series.

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