![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Towards a turnkey approach to unbiased Monte Carlo estimation of smooth functions of expectations
![]() Towards a turnkey approach to unbiased Monte Carlo estimation of smooth functions of expectationsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact nobody. DMLW01 - International workshop on diffusions in machine learning: foundations, generative models, and optimisation Given a smooth function $f$, we develop a general approach to turn Monte Carlo samples with expectation $m$ into an unbiased estimate of $f(m)$. Specifically, we develop estimators that are based on randomly truncating the Taylor series expansion of $f$ and estimating the coefficients of the truncated series. We derive their properties and propose a strategy to set their tuning parameters—which depend on $m$—automatically, with a view to make the whole approach simple to use. We develop our methods for the specific functions $f(x)=\log x$ and $f(x)=1/x$, as they arise in several statistical applications such as maximum likelihood estimation of latent variable models and Bayesian inference for un-normalised models.Detailed numerical studies are performed for a range of applications to determine how competitive and reliable the proposed approach is. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsJewish History MSM-AIMR Joint Online Workshop 2020 Fitzwilliam MuseumOther talksDiscussion Coffee Active Particles Learning Functionalities Policy Theme Lunch in Julian Study Centre foyer (ground floor) Moments of partition functions of 2D Gaussian polymers in the weak disorder regime |