Miklos Redei (London School of Economics) will be visiting the Geneva Symmetry Group (GSG) this week. The GSG is happy to present two talks by him, a more formal talk on Wednesday, and an informal seminar on Thursday (abstracts below):
Wednesday, 17 April 2019, at 18:15 in Room L208 (Landolt):
Miklos Redei (London School of Economics): Categorial local quantum physics
Thursday, 18 April 2019, at 16:15 in room B002 (Bastions):
Miklos Redei (London School of Economics): Some features of Bayesian learning based on conditioning using conditional expectations
Anyone who wishes to attend is welcome.
Wednesday, 17 April 2019 in Room L208 (Landolt) – Miklos Redei (London School of Economics): Categorial local quantum physics
Abstract: The talk reviews the basic ideas of the categorial approach to quantum field theory initiated by Brunetti, Fredenhagen and Verch (2003). The key concept in this approach is a covariant functor representing the quantum field. The functor is between the category of physically reasonable spactimes and the category of C* algebras representing observables localized in a given spacetime. The causal independence of spacelike separated quantum systems is implemented in this framework by imposing locality conditions on the covariant functor. In addition to discussing the usual locality conditions, the talk presents a purely categorial notion of subobject independence in a general category. It is argued that specifying the suggested categorial subobject independence concept in terms of the category of operator algebras with operations as morphisms one obtains an independence condition that should be postulated for the covariant functor to hold in order to express physical locality in categorial local quantum field theory.
In terms of technical difficulty, this talk rates 4/5.
Reference: Z. Gyenis & M. Redei: “Categorial Subsystem Independence as Morphism Co-possibility”, Communications in Mathematical Physics, vol. 357 (2018) 447–465, https://doi.org/10.1007/s00220-017-2940-8
Thursday, 18 April 2019 in Room B002 (Bastions) – Miklos Redei (London School of Economics): Some features of Bayesian learning based on conditioning using conditional expectations
Abstract: The talk takes Bayesian learning as an inference whereby one infers probabilities from other probabilities (evidence) using conditionalization of probability measures as the inference device. The general form of this kind of inference is based on the concept of conditional expectation introduced into probability theory by Kolmogorov (1933). This kind of (Bayesian) conditionalization defines a two-place “Bayes accessibility” relation in the set of probability measures on a Boolean algebra. Characterizing the Bayes accessibility relation amounts to characterizing Bayesian learning. Basic features of the Bayes accessibility relation will be presented in the talk. Special attention will be paid to properties of the “Bayes Blind Spot”: The Bayes Blind Spot is, by definition, the set of probability measures on a Boolean algebra that are absolutely continuous with respect to the prior of a Bayesian Agent and which the Bayesian Agent cannot learn by a single conditionalization no matter what (possibly uncertain) evidence he has about the elements in the Boolean algebra. The size of the Bayes Blind Spot is inverse proportional to the strength of Bayesian Learning: The larger the Bayes Blind Spot the weaker the Bayesian learning. In the talk propositions on the size of the Bayes Blind Spot will be presented.
Relevant paper: Z. Gyenis & M. Redei: “General properties of Bayesian learning as statistical inference determined by conditional expectations”, The Review of Symbolic Logic vol. 10 (2017) 719-755, doi: 10.1017/S1755020316000502; and preprint: http://philsci-archive.pitt.edu/12326/