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Ritabrata Dutta - Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization

Dates:16 February 2022
Times:14:00 - 15:00
What is it:Seminar
Organiser:Department of Mathematics
Who is it for:University staff, External researchers, Current University students
Speaker:Ritabrata Dutta
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  • Department of Mathematics

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  • In category "Seminar"
  • In group "(Maths) Probability and statistics"
  • By Department of Mathematics

Ritabrata Dutta, Assistant Professor of Statistics at the Department of Statistics, University of Warwick is our speaker for the Statistics seminar series.

Title: Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization

Abstract: Probabilistic forecasting consists of stating a probability distribution for a future outcome based on past observations. In meteorology, ensembles of physics-based numerical models are run to get such distribution. Usually, performance is evaluated with scoring rules, functions of the forecast distribution and the observed outcome. With some scoring rules, calibration and sharpness of the forecast can be assessed at the same time. In deep learning, generative neural networks parametrize distributions on high-dimensional spaces and easily allow sampling by transforming draws from a latent variable. Conditional generative networks additionally constrain the distribution on an input variable. In this manuscript, we perform probabilistic forecasting with conditional generative networks trained to minimize scoring rule values. In contrast to Generative Adversarial Networks (GANs), no discriminator is required and training is stable. We perform experiments on two chaotic models and a global dataset of weather observations; results are satisfactory and better calibrated than what achieved by GANs.

Speaker

Ritabrata Dutta

Organisation: University of Warwick

  • https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/dutta/

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Zoom link: https://zoom.us/j/92947173491

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Olatunji Johnson

olatunji.johnson@manchester.ac.uk

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