nLab sober measurable space

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Context

Physics

physics, mathematical physics, philosophy of physics

Surveys, textbooks and lecture notes


theory (physics), model (physics)

experiment, measurement, computable physics

Measure and probability theory

Contents

Idea

Sober measurable spaces are to measurable spaces as sober topological spaces are to topological spaces.

More in detail, a measurable space is sober if and only if every zero-one measure on it is a Dirac delta at a unique point. Equivalently, if its sigma-algebra separates points, and if every irreducible measure is induced by a point. (See below.)

This allows to draw parallels between measure theory and point-free topology.

Definition

A measurable space XX is called sober if any of the following equivalent conditions hold:

where PP denotes the Giry monad and δ\delta is its unit, given by Dirac deltas. (Note that the diagram is always a fork, by naturality of δ\delta.)

The last condition is sometimes called the equalizing requirement (Moggi’91).

Analogy with the topological case

Compare the definition with the following equivalent conditions in order for a topological space to be sober:

where VV denotes the monad of closed subsets (with the lower Vietoris topology?) and σ\sigma is its unit, given by (closures of) singletons.

Examples

Categorical properties

Sober measurable spaces form a reflective subcategory of Meas. That is, the inclusion functor SoberMeasMeasSoberMeas\hookrightarrow Meas has a left adjoint S:MeasSoberMeasS:Meas\to SoberMeas. It maps a measurable space XX to the space of zero-one measures over it, which is equivalently given by the following equalizer, where PP denotes the Giry monad and δ\delta is its unit.

Analogously to the case of topological spaces, this functor is sometimes called the sobrification.

To see its universal property, notice that by naturality of δ\delta, and by definition of equalizer, there is a unique map XSXX\to S X making the triangle below commute. This map XSXX\to S X forms the unit of the adjunction.

(More on this at zero-one measure.)

Separating points and having enough points

A sigma-algebra 𝒜\mathcal{A} on a set XX is said to separate points if any of the following equivalent conditions holds:

  • Given x,yXx,y\in X, x=yx=y if and only if for all A𝒜A\in\mathcal{A}, xAx\in A if and only if yAy\in A.
  • Given x,yXx,y\in X, x=yx=y if and only if for every measurable function f:(X,𝒜)f:(X,\mathcal{A})\to\mathbb{R}, f(x)=f(y)f(x)=f(y).
  • Given x,yXx,y\in X, x=yx=y if and only if for every Markov kernel k:(X,𝒜)(Y,)k:(X,\mathcal{A})\to (Y,\mathcal{B}) and every BB\in\mathcal{B}, k(B|x)=k(B|y)k(B|x)=k(B|y).
  • The Dirac delta map (unit of the Giry monad) δ:XPX\delta:X\to P X, xδ xx\mapsto\delta_x is injective.

This condition is analogous to the T0 (Kolmogorov) separation axiom for topological spaces: it says that the sigma-algebra can distinguish any two points.

One can also define an equivalence relation analogous to the specialization preorder, as follows:

xyA𝒜,xAyA. x\sim y \qquad\Leftrightarrow\qquad \forall A\in\mathcal{A}, x\in A \Rightarrow y\in A .

(Note that the relation is symmetric because sigma-algebras, unlike topologies, are closed under complements.) A sigma-algebra separates points precisely if this relation is discrete.

Every sober measurable space has a sigma-algebra which separates points. Just as for topological spaces, however, separating points is not enough: one also needs a condition of having enough points.

Definition

We say that a measurable space (X,𝒜)(X,\mathcal{A}) has enough points if and only if every zero-one measure is a Dirac measure.

This way, a measurable space is sober if and only if it separates points and it has enough points.

Here is another analogy with topological spaces: the sobrification process can be taken in two steps: First, we quotient under the relation of “being indistinguishable for the sigma-algebra” (analogous to the Kolmogorov quotient). Then we “add the missing points”.

(Compare again with the topological case.)

References

  • Sean Moss, Paolo Perrone, Probability monads with submonads of deterministic states, LICS 2022. (arXiv:2204.07003)

  • Anna Bucalo and Giuseppe Rosolini, Sobriety for equilogical spaces. Theorerical Computer Science 546, 2014. (doi)

  • Paul Taylor, Sober spaces and continuations. Theory and Applications of Categories 10(12), 2002. (link)

  • Eugenio Moggi, Notions of computations and monads, Information and Computation 93(1), 1991 (LICS 1989).

Introductory material

category: probability

Last revised on September 28, 2024 at 17:12:00. See the history of this page for a list of all contributions to it.