Reference : Leveraging Global Parameters for Flow-based Neural Posterior Estimation
E-prints/Working papers : Already available on another site
Physical, chemical, mathematical & earth Sciences : Mathematics
Engineering, computing & technology : Computer science
Leveraging Global Parameters for Flow-based Neural Posterior Estimation
Rodrigues, Pedro []
Moreau, Thomas []
Louppe, Gilles mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data >]
Gramfort, Alexandre []
[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.

File(s) associated to this reference

Fulltext file(s):

Open access
2102.06477v1.pdfAuthor preprint1.56 MBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.