In
mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics ...
, a
subset
In mathematics, Set (mathematics), set ''A'' is a subset of a set ''B'' if all Element (mathematics), elements of ''A'' are also elements of ''B''; ''B'' is then a superset of ''A''. It is possible for ''A'' and ''B'' to be equal; if they are ...
of a
Polish space is universally measurable if it is
measurable with respect to every
complete probability measure
In mathematics, a probability measure is a real-valued function defined on a set of events in a probability space that satisfies measure properties such as ''countable additivity''. The difference between a probability measure and the more gener ...
on
that measures all
Borel subsets of
. In particular, a universally measurable set of
reals is necessarily
Lebesgue measurable (see below).
Every
analytic set is universally measurable. It follows from
projective determinacy, which in turn follows from sufficient
large cardinal
In the mathematical field of set theory, a large cardinal property is a certain kind of property of transfinite cardinal numbers. Cardinals with such properties are, as the name suggests, generally very "large" (for example, bigger than the least � ...
s, that every
projective set is universally measurable.
Finiteness condition
The condition that the measure be a
probability measure
In mathematics, a probability measure is a real-valued function defined on a set of events in a probability space that satisfies measure properties such as ''countable additivity''. The difference between a probability measure and the more gener ...
; that is, that the measure of
itself be 1, is less restrictive than it may appear. For example, Lebesgue measure on the reals is not a probability measure, yet every universally measurable set is Lebesgue measurable. To see this, divide the real line into countably many intervals of length 1; say, ''N''
0=
[0,1), ''N''
1=
[1,2), ''N''
2=
[-1,0), ''N''
3=
[2,3), ''N''
4=
[-2,-1), and so on. Now letting μ be Lebesgue measure, define a new measure ν by
:
Then easily ν is a probability measure on the reals, and a set is ν-measurable if and only if it is Lebesgue measurable. More generally a universally measurable set must be measurable with respect to every sigma-finite measure that measures all Borel sets.
Example contrasting with Lebesgue measurability
Suppose
is a subset of Cantor space
; that is,
is a set of infinite
sequences of zeroes and ones. By putting a binary point before such a sequence, the sequence can be viewed as a
real number between 0 and 1 (inclusive), with some unimportant ambiguity. Thus we can think of
as a subset of the interval
,1/nowiki>, and evaluate its Lebesgue measure
In measure theory, a branch of mathematics, the Lebesgue measure, named after French mathematician Henri Lebesgue, is the standard way of assigning a measure to subsets of ''n''-dimensional Euclidean space. For ''n'' = 1, 2, or 3, it coincides wit ...
, if that is defined. That value is sometimes called the coin-flipping measure of , because it is the probability of producing a sequence of heads and tails that is an element of upon flipping a fair coin infinitely many times.
Now it follows from the axiom of choice that there are some such without a well-defined Lebesgue measure (or coin-flipping measure). That is, for such an , the probability that the sequence of flips of a fair coin will wind up in is not well-defined. This is a pathological property of that says that is "very complicated" or "ill-behaved".
From such a set , form a new set by performing the following operation on each sequence in : Intersperse a 0 at every even position in the sequence, moving the other bits to make room. Although is not intuitively any "simpler" or "better-behaved" than , the probability that the sequence of flips of a fair coin will be in is well-defined. Indeed, to be in , the coin must come up tails on every even-numbered flip, which happens with probability zero.
However is ''not'' universally measurable. To see that, we can test it against a ''biased'' coin that always comes up tails on even-numbered flips, and is fair on odd-numbered flips. For a set of sequences to be ''universally'' measurable, an arbitrarily ''biased'' coin may be used (even one that can "remember" the sequence of flips that has gone before) and the probability that the sequence of its flips ends up in the set must be well-defined. However, when is tested by the coin we mentioned (the one that always comes up tails on even-numbered flips, and is fair on odd-numbered flips), the probability to hit is not well defined (for the same reason why cannot be tested by the fair coin). Thus, is ''not'' universally measurable.
References
*
* {{citation
, author= Nishiura Togo
, title= Absolute Measurable Spaces
, publisher= Cambridge University Press
, year= 2008
, isbn= 0-521-87556-0
, url-access= registration
, url= https://archive.org/details/absolutemeasurab0000nish
Descriptive set theory
Determinacy
Measure theory