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[Fizinfo] Stat Fiz Szeminarium

Chronological Thread 
  • From: StatFizSzeminar <statfiz AT>
  • To: fizinfo AT
  • Subject: [Fizinfo] Stat Fiz Szeminarium
  • Date: Tue, 02 Oct 2018 07:07:10 +0200

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ELTE TTK Fizikai Intézet

2018. október 3.
szerda, 11.00

Michael C. Abbott

Jagiellonian University

"Maximizing the information learned from finite data
selects a simple model"

We use the language of uninformative Bayesian prior
choice to study the selection of appropriately simple
effective models. We advocate for the prior which max-
imizes the mutual information between parameters and
predictions, learning as much as possible from limited
data. When many parameters are poorly constrained by
the available data, we find that this prior puts weight
only on boundaries of the parameter manifold. Thus it
selects a lower-dimensional effective theory in a prin-
cipled way, ignoring irrelevant parameter directions.
In the limit where there is sufficient data to tightly
constrain any number of parameters, this reduces to
Jeffreys prior. But we argue that this limit is patho-
logical when applied to the hyper-ribbon parameter man-
ifolds generic in science, because it leads to dramatic
dependence on effects invisible to experiment.

1117, Budapest, Pázmány P. sétány 1/A, Északi tömb 2.54

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