In probability theory, a (usually infinite) sequence is called exchangeable, or sometimes symmetric, if it is invariant under finite permutations. In particular:
An exchangeable probability measure is a measure on a product space which is invariant under finite permutations of the coordinates;
An exchangeable event is an event (measurable subset) of a product space which is invariant under finite permutations.
(See below for more details.)
Exchangeable random variables and events play a prominent role in de Finetti's theorem and in the Hewitt-Savage zero-one law.
Let $X$ be a measurable space, and consider the infinite product $X^{\mathbb{N}}$. A finite permutation is an isomorphism $\sigma \colon X^{\mathbb{N}}\to X^{\mathbb{N}}$ which is a permutation of finitely many components of $X^{\mathbb{N}}$. (Equivalently, it is induced by finitely many applications of the braiding.)
A probability measure $p$ on $X^{\mathbb{N}}$ is called exchangeable or symmetric if and only if it is invariant under all finite permutations. In other words, if for all finite permutations $\sigma:X^{\mathbb{N}}\to X^{\mathbb{N}}$, the pushforward measure $\sigma_*p$ is equal to $p$. Even more explicitly, for every measurable subset $A$ of $X^{\mathbb{N}}$, we have
(Note that such a probability measure is often specified by its finite marginals, via the Kolmogorov extension theorem.)
A sequence of random variables or random elements $f_n:\Omega\to X$ on a probability space $(\Omega,\mu)$ is said to be exchangeable, or to form an exchangeable process, if the resulting joint distribution on $X^{\mathbb{N}}$ is an exchangeable measure.
Similar definitions can be given for a finite product $X^n$.
In a Markov category, and more generally in any symmetric monoidal category, one can similarly define an exchangeable state to be a morphism $I\to X^\mathbb{N}$ from the monoidal unit to an infinite tensor product which is invariant under the action of the braiding.
In other words, finite permutations form a group acting on $X^{\mathbb{N}}$, and exchangeable states form a cone over the resulting diagram. De Finetti's theorem can be interpreted as saying that the limit cone is given by a space of probability measure (such as given by probability monad).
A sequence of iid random variables is exchangeable.
Somewhat on the other extreme, perfectly correlated? random variables are also exchangeable, but in general far from independent. Their joint distribution is in the form
for some distribution $q$ on $X$.
Similarly to measures, an event (measurable subset) $A$ of the infinite product $X^{\mathbb{N}}$ is called an exchangeable or symmetric event if for all finite permutations $\sigma:X^{\mathbb{N}}\to X^{\mathbb{N}}$,
i.e. if it is invariant under finite permutations.
The exchangeable events of $X^{\mathbb{N}}$ form a sigma-algebra, called the exchangeable sigma-algebra. It is the invariant sigma-algebra for the action of finite permutations on $X^{\mathbb{N}}$.
In some contexts one is, instead, interested in events which are invariant only almost surely, that is, those events $A$ such that for all finite permutations $\sigma$,
These events are sometimes called almost exchangeable, almost surely exchangeable, or even just exchangeable.
They also form a sigma-algebra, also often called the exchangeable sigma-algebra.
In probability theory, very often, this ambiguity in terminology does not cause problems, since, at least for countably many random variables, it is customary to only look at processes up to almost sure equality.
In categorical probability, often the exchangeable sigma-algebra can be encoded as a colimit of the action of permutations. (The same can be said about invariant sigma-algebras of more general actions.)
In Stoch and in Markov categories, the sigma-algebra of strictly (not just almost surely) invariant sets can be seen as a colimit, both in the category of Markov kernels and of measurable functions (Moss-Perrone’23).
Similarly, in Krn and in categories of couplings, the sigma-algebra of almost surely invariant sets can be seen as a colimit compatible with the dagger structure (Ensarguet-Perrone’23). For standard Borel spaces, one can, up to isomorphism, also consider strictly invariant sets.
De Finetti's theorem says that every exchangeable probability measure is a unique convex mixture of iid ones.
Another way of stating de Finetti’s theorem is that an infinite sequence of exchangeable random variables are conditionally independent given the exchangeable sigma-algebra.
The Hewitt-Savage zero-one law says that given iid random variables, the probability of each exchangeable event is either zero or one, i.e. it is a zero-one measure.
The action of finite permutations on $X^\mathbb{N}$ can be seen as a special case of (generalized) shifts.
Wikipedia, Exchangeable random variables
Bart Jacobs, Sam Staton, De Finetti’s construction as a categorical limit, Proceedings of CMCS, 2021. (arXiv:2003.01964)
Tobias Fritz, Tomáš Gonda, Paolo Perrone, De Finetti’s theorem in categorical probability. Journal of Stochastic Analysis, 2021. (arXiv:2105.02639)
Sean Moss, Paolo Perrone, A category-theoretic proof of the ergodic decomposition theorem, Ergodic Theory and Dynamical Systems, 2023. (arXiv:2207.07353)
Noé Ensarguet, Paolo Perrone, Categorical probability spaces, ergodic decompositions, and transitions to equilibrium, arXiv:2310.04267
Last revised on July 21, 2024 at 14:49:00. See the history of this page for a list of all contributions to it.