Probability space

In probability theory, the notion of probability space is the conventional mathematical model of randomness. It formalizes three interrelated ideas by three mathematical notions. First, a sample point (called also elementary event), — something to be chosen at random (outcome of experiment, state of nature, possibility etc.) Second, an event, — something that will occur or not, depending on the chosen sample point. Third, the probability of an event.

Alternative models of randomness (finitely additive probability, non-additive probability) are sometimes advocated in connection to various probability interpretations.

Introduction

The notion "probability space" provides a basis of the formal structure of probability theory. It may puzzle a non-mathematician, since

• it is called "space" but is far from geometry;

• it is said to provide a basis, but many people applying probability theory in practice neither understand nor need this quite technical notion.

These puzzling facts are explained below. First, a mathematical definition is given; it is quite technical, but the reader may skip it. Second, an elementary case (finite probability space) is presented. Third, the puzzling facts are explained. Next topics are countably infinite probability spaces, and general probability spaces.

Definition

A probability space is a measure space such that the measure of the whole space is equal to 1.

In other words: a probability space is a triple $\textstyle (\Omega ,{\mathcal {F}},P)$ consisting of a set $\textstyle \Omega$ (called the sample space), a σ-algebra (called also σ-field) $\textstyle {\mathcal {F}}$ of subsets of $\textstyle \Omega$ (these subsets are called events), and a measure $\textstyle P$ on $\textstyle (\Omega ,{\mathcal {F}})$ such that $\textstyle P(\Omega )=1$ (called the probability measure).

Elementary level: finite probability space

On the elementary level, a probability space consists of a finite number $n$ of sample points $\omega _{1},\dots ,\omega _{n}$ and their probabilities $p_{1},\dots ,p_{n}$ — positive numbers satisfying $p_{1}+\dots +p_{n}=1.$ The set $\Omega =\{\omega _{1},\dots ,\omega _{n}\}$ of all sample points is called the sample space. Every subset $A\subset \Omega$ of the sample space is called an event; its probability is the sum of probabilities of its sample points. For example, if $A=\{\omega _{1},\omega _{8},\omega _{9}\}$ then $\mathbb {P} (A)=p_{1}+p_{8}+p_{9}$.

A random variable $X$ is described by real numbers $x_{1},\dots ,x_{n}$ (not necessarily different) corresponding to the sample points $\omega _{1},\dots ,\omega _{n}.$ Its expectation is $\mathbb {E} (X)=x_{1}p_{1}+\dots +x_{n}p_{n}.$

The puzzling facts explained

Why "space"?

Fact: it is called "space" but is far from geometry.

Explanation: see Space (mathematics).

What is it good for?

Fact: it is said to provide a basis, but many people applying probability theory in practice do not need this notion. For them, formulas (such as the addition rule, the multiplication rule, the inclusion-exclusion rule, the law of total probability, Bayes' rule etc.) are instrumental; probability spaces are not, they reign but do not rule.

Explanation 1. Likewise, one may say that points are of no use in geometry. Formulas connecting lengths and angles (such as Pythagorean theorem, law of sines etc.) are instrumental; points are not.

However, these useful formulas follow from the axioms of geometry formulated in terms of points (and some other notions). It would be very cumbersome and unnatural, if at all possible, to reformulate geometry avoiding points.

Similarly, the formulas of probability follow from the axioms of probability formulated in terms of probability spaces. It would be very cumbersome and unnatural, if at all possible, to reformulate probability theory avoiding probability spaces.

Explanation 2. One of the most useful formulas is linearity of expectation: $\mathbb {E} (aX+bY)=a\mathbb {E} (X)+b\mathbb {E} (Y)$ whenever $X,Y$ are random variables and $a,b$ are (non-random) coefficients. One may derive this formula avoiding probability spaces, by transforming the sum

$\sum _{x,y}(ax+by)\mathbb {P} (X=x,Y=y)$

into the linear combination

$a\sum _{x}x\mathbb {P} (X=x)+b\sum _{y}y\mathbb {P} (Y=y).$

However, much better insight is provided by probability spaces: the expectation $\mathbb {E} (X)=x_{1}p_{1}+\dots +x_{n}p_{n}$ is a linear function of the variables $x_{1},\dots ,x_{n}.$ Moreover, a helpful connection to linear algebra appears: random variables form an $n$-dimensional linear space, and the expectation is a linear functional on this space.

Two approaches to infinity

Everything is finite in applications, but mathematical theories often benefit by using infinity. In mathematical analysis, infinity appears only indirectly, via limiting procedure, when one says that something "tends to infinity". In the set theory, infinity appears directly; for instance, one say that "the set of prime numbers is infinite". Both approaches to infinity can be used in probability theory.

Example 1. "A randomly chosen positive integer is even with probability 0.5." This phrase is interpreted via limiting procedure: the fraction of even numbers among $1,\dots ,n$ converges to 0.5 as $n$ tends to infinity. This approach introduces an infinite sequence of finite probability spaces; the $n$-th space consists of sample points $1,\dots ,n$ endowed with equal probabilities $1/n.$

Example 2. "Flipping a fair coin repeatedly one must get "heads" sooner or later." Also this phrase may be interpreted via an infinite sequence of finite probability spaces: flipping the coin $n$ times one gets "heads" at least once with the probability $1-2^{-n}$ that converges to 1 as $n$ tends to infinity. Another interpretation is possible, via a single infinite probability space consisting of the sequences H, TH, TTH, TTTH and so on ("TTH" means: "tails" twice, then "heads"; the coin is tossed until "heads") having the probabilities

$\mathbb {P} (H)=1/2;\quad \mathbb {P} (TH)=1/4;\quad \mathbb {P} (TTH)=1/8;\quad \dots$

whose sum is $2^{-1}+2^{-2}+2^{-3}+\dots =1.$ One may insert also the infinite sequence "TTT..." ("tails forever") to the sample space; but then necessarily

$\mathbb {P} (TTT\dots )=0$

since the sum of probabilities cannot exceed 1.

It is tempting to extend this approach (a single infinite probability space) to the case of Example 1, defining

$\mathbb {P} (A)=\lim _{n\to \infty }{\frac {|A\cap \{1,\dots ,n\}|}{n}}$

for $A\subset \Omega =\{1,2,\dots \};$ here $|A\cap \{1,\dots ,n\}|$ is the number of elements of $A$ among $1,\dots ,n.$ This limit, called the density of $A,$ is a useful mathematical device. However, treating it as probability one gets numerous paradoxes. One paradox: a positive integer chosen at random must have more than one decimal digit, since $\mathbb {P} (\{1,\dots ,9\})=0.$ Similarly, it must have more than two digits; and so on. Thus, it must have infinitely many digits, which cannot happen to an integer. Another paradox: let two positive integers $X,Y$ be chosen at random, independently. Then $\mathbb {P} (X\leq Y)=0,$ since $\mathbb {P} (X\leq 1)=0,$ $\mathbb {P} (X\leq 2)=0$ and so on. Similarly, $\mathbb {P} (Y\leq X)=0.$ Thus, it must be $X>Y>X$.

By default (unless explicitly stated otherwise), probability theory deals with a single probability space. When solving a specific problem, the probability space is usually (but not always) chosen according to the given problem; when developing general theory, it is arbitrary.

The notions "negligible" and "almost sure"

A sample point of zero probability can be added to a probability space or removed from it at will, since it cannot contribute to any probability (or expectation). Such point is called negligible.

In Example 2 (above) the case "tails forever" is negligible.

An event of probability 1 is said to happen almost surely.

In Example 2 (above), "heads" appears (sooner or later) almost surely.

The following anecdote follows a real event.

Professor (dealing with a random variable $X$): ...here we use the evident fact that $-1\leq \sin X\leq 1$ almost surely.

Student: Why "almost surely"? It holds surely.

Professor (laughing): You see, I am a probabilist. We probabilists do not say "sure"; "almost sure" is our strongest expression.

Countable additivity

As was noted above, paradoxes prevent treating the density of a set $A\subset \Omega =\{1,2,\dots \}$ as its probability. These paradoxes are caused by violation of countable additivity. Namely, single-point sets $A_{1}=\{1\},\,A_{2}=\{2\},\,A_{3}=\{3\},\dots$ are of density 0 (each), but their union $A_{1}\cup A_{2}\cup \dots =\Omega$ is of density 1.

The countable additivity requires

$\mathbb {P} (A_{1}\cup A_{2}\cup \dots )=\mathbb {P} (A_{1})+\mathbb {P} (A_{2})+\dots$

whenever events $A_{1},A_{2},\dots$ are mutually excluding (in other words, disjoint sets).

Countable additivity is an axiom of probability theory.

For a random choice of an integer, the countable additivity implies that the probability of a set is the sum of probabilities of its elements,

$\mathbb {P} (A)=\sum _{n\in A}p_{n}.$

This is a finite sum for a finite $A,$ but an infinite series for an infinite $A.$ The order of terms does not matter, since all terms are nonnegative. The series converges, since its partial sums cannot exceed 1. For example, the probability of being even:

$\mathbb {P} (A)=p_{2}+p_{4}+p_{6}+\dots \qquad {\text{for }}A=\{2,4,6,\dots \}.$

The numbers $p_{n}$ must satisfy

$p_{n}\geq 0;\qquad p_{1}+p_{2}+p_{3}+\dots =1.$

Otherwise, they are arbitrary; every sequence of numbers satisfying these conditions leads to a probability space.

The case of equal probabilities, $p_{1}=p_{2}=p_{3}=\dots ,$ is impossible, since the series $p+p+p+\dots$ never converges to 1; it converges to 0 if $p=0$ and diverges (to infinity) if $p>0.$ Thus, the phrase "an integer chosen at random" is meaningless if a probability distribution on the integers is not specified. "The uniform distribution on the integers" does not exist.

Nonatomic probability space

Endless tossing of a fair coin is a classical object of probability theory. The weak law of large numbers, the strong law of large numbers, the central limit theorem, — they all were developed first for this special case, and letter generalized.

Many textbooks in probability explain only (finite and) countable probability spaces, but do not hesitate to write "Consider an infinite sequence $A_{1},A_{2},\dots$ of independent events of probability $1/2$". The problem is that existence of $n$ such events $A_{1},\dots ,A_{n}$ implies that each sample point is of probability $\leq 2^{-n};$ thus, existence of the infinite sequence $A_{1},A_{2},\dots$ implies that each sample point is of probability zero! In a (finite or) countable probability space this situation is impossible by countable additivity.