Reference : Leveraging Global Parameters for Flow-based Neural Posterior Estimation
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Physical, chemical, mathematical & earth Sciences : Mathematics
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/257249
Leveraging Global Parameters for Flow-based Neural Posterior Estimation
English
Rodrigues, Pedro []
Moreau, Thomas []
Louppe, Gilles mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data >]
Gramfort, Alexandre []
12-Feb-2021
No
[en] Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. This arises in many practical situations, such as when inferring the distance and power of a radio source (is the source close and weak or far and strong?) or when estimating the amplifier gain and underlying brain activity of an electrophysiological experiment. In this work, we present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters. Our method extends recent developments in simulation-based inference(SBI) based on normalizing flows to Bayesian hierarchical models. We validate quantitatively our proposal on a motivating example amenable to analytical solutions, and then apply it to invert a well known non-linear model from computational neuroscience.
Researchers
http://hdl.handle.net/2268/257249
https://arxiv.org/abs/2102.06477
https://arxiv.org/abs/2102.06477

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