Our first speaker is Carlo
Albert, who is talking about "Learning summary statistics for Bayesian inference with Autoencoders" on Thursday the 28th September
at 9am UK time, with an abstract reported below.
The talk will be streamed on Zoom
Password:
594071
We
are looking forward to seeing you next Thursday,
best,
Massimiliano on the behalf of the
OneWorldABC Organisers
Date: Thursday,
September 28, 9am UK time
Speaker: Carlo
Albert, Swiss Federal Institute of Aquatic Science and Technology
Title: Learning
summary statistics for Bayesian inference with Autoencoders
Abstract: In
order for ABC to give accurate results and be efficient, we need summary statistics that retain most of the parameter-related information and cancel out most of the noise, respectively. For many scientific applications, we need strictly more summary statistics
than model parameters to reach a satisfactory approximation of the posterior. Therefore, we propose to use a latent representation of deep neural networks based on Autoencoders as summary statistics. To create an incentive for the encoder to encode all the
parameter-related information but not the noise, we give the decoder access to explicit or implicit information on the noise that has been used to generate the training data. We validate the approach empirically on two types of stochastic models, one being
a member of the exponential family, the other one not.
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Dr. Massimiliano Tamborrino
Associate Professor
Department of Statistics