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In
probability theory Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expre ...
, the conditional expectation, conditional expected value, or conditional mean of a
random variable A random variable (also called random quantity, aleatory variable, or stochastic variable) is a Mathematics, mathematical formalization of a quantity or object which depends on randomness, random events. The term 'random variable' in its mathema ...
is its
expected value In probability theory, the expected value (also called expectation, expectancy, expectation operator, mathematical expectation, mean, expectation value, or first Moment (mathematics), moment) is a generalization of the weighted average. Informa ...
evaluated with respect to the
conditional probability distribution In probability theory and statistics, the conditional probability distribution is a probability distribution that describes the probability of an outcome given the occurrence of a particular event. Given two jointly distributed random variables X ...
. If the random variable can take on only a finite number of values, the "conditions" are that the variable can only take on a subset of those values. More formally, in the case when the random variable is defined over a discrete
probability space In probability theory, a probability space or a probability triple (\Omega, \mathcal, P) is a mathematical construct that provides a formal model of a random process or "experiment". For example, one can define a probability space which models ...
, the "conditions" are a partition of this probability space. Depending on the context, the conditional expectation can be either a random variable or a function. The random variable is denoted E(X\mid Y) analogously to
conditional probability In probability theory, conditional probability is a measure of the probability of an Event (probability theory), event occurring, given that another event (by assumption, presumption, assertion or evidence) is already known to have occurred. This ...
. The function form is either denoted E(X\mid Y=y) or a separate function symbol such as f(y) is introduced with the meaning E(X\mid Y) = f(Y).


Examples


Example 1: Dice rolling

Consider the roll of a fair die and let ''A'' = 1 if the number is even (i.e., 2, 4, or 6) and ''A'' = 0 otherwise. Furthermore, let ''B'' = 1 if the number is prime (i.e., 2, 3, or 5) and ''B'' = 0 otherwise. The unconditional expectation of A is E = (0+1+0+1+0+1)/6 = 1/2, but the expectation of A ''conditional'' on ''B'' = 1 (i.e., conditional on the die roll being 2, 3, or 5) is E \mid B=1(1+0+0)/3=1/3, and the expectation of A conditional on ''B'' = 0 (i.e., conditional on the die roll being 1, 4, or 6) is E \mid B=0(0+1+1)/3=2/3. Likewise, the expectation of B conditional on A = 1 is E \mid A=1 (1+0+0)/3=1/3, and the expectation of ''B'' conditional on ''A'' = 0 is E \mid A=0(0+1+1)/3=2/3.


Example 2: Rainfall data

Suppose we have daily rainfall data (mm of rain each day) collected by a weather station on every day of the ten-year (3652-day) period from January 1, 1990, to December 31, 1999. The unconditional expectation of rainfall for an unspecified day is the average of the rainfall amounts for those 3652 days. The ''conditional'' expectation of rainfall for an otherwise unspecified day known to be (conditional on being) in the month of March, is the average of daily rainfall over all 310 days of the ten–year period that fall in March. Similarly, the conditional expectation of rainfall conditional on days dated March 2 is the average of the rainfall amounts that occurred on the ten days with that specific date.


History

The related concept of
conditional probability In probability theory, conditional probability is a measure of the probability of an Event (probability theory), event occurring, given that another event (by assumption, presumption, assertion or evidence) is already known to have occurred. This ...
dates back at least to
Laplace Pierre-Simon, Marquis de Laplace (; ; 23 March 1749 – 5 March 1827) was a French polymath, a scholar whose work has been instrumental in the fields of physics, astronomy, mathematics, engineering, statistics, and philosophy. He summariz ...
, who calculated conditional distributions. It was
Andrey Kolmogorov Andrey Nikolaevich Kolmogorov ( rus, Андре́й Никола́евич Колмого́ров, p=ɐnˈdrʲej nʲɪkɐˈlajɪvʲɪtɕ kəlmɐˈɡorəf, a=Ru-Andrey Nikolaevich Kolmogorov.ogg, 25 April 1903 – 20 October 1987) was a Soviet ...
who, in 1933, formalized it using the
Radon–Nikodym theorem In mathematics, the Radon–Nikodym theorem is a result in measure theory that expresses the relationship between two measures defined on the same measurable space. A ''measure'' is a set function that assigns a consistent magnitude to the measurab ...
. In works of
Paul Halmos Paul Richard Halmos (; 3 March 1916 – 2 October 2006) was a Kingdom of Hungary, Hungarian-born United States, American mathematician and probabilist who made fundamental advances in the areas of mathematical logic, probability theory, operat ...
and Joseph L. Doob from 1953, conditional expectation was generalized to its modern definition using sub-''σ''-algebras.


Definitions


Conditioning on an event

If is an event in \mathcal with nonzero probability, and is a
discrete random variable A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. The term 'random variable' in its mathematical definition refers ...
, the conditional expectation of given is : \begin \operatorname (X \mid A) &= \sum_x x P(X = x \mid A) \\ & =\sum_x x \frac \end where the sum is taken over all possible outcomes of . If P(A) = 0, the conditional expectation is undefined due to the division by zero.


Discrete random variables

If and are
discrete random variable A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. The term 'random variable' in its mathematical definition refers ...
s, the conditional expectation of given is : \begin \operatorname (X \mid Y=y) &= \sum_x x P(X = x \mid Y = y) \\ &= \sum_x x \frac \end where P(X = x, Y = y) is the joint probability mass function of and . The sum is taken over all possible outcomes of . As above, the expression is undefined if P(Y=y) = 0. Conditioning on a discrete random variable is the same as conditioning on the corresponding event: :\operatorname (X \mid Y=y) = \operatorname (X \mid A) where is the set \.


Continuous random variables

Let X and Y be
continuous random variable In probability theory and statistics, a probability distribution is a function that gives the probabilities of occurrence of possible events for an experiment. It is a mathematical description of a random phenomenon in terms of its sample spa ...
s with joint density f_(x,y), Y's density f_(y), and conditional density \textstyle f_(x\mid y) = \frac of X given the event Y=y. The conditional expectation of X given Y=y is : \begin \operatorname (X \mid Y=y) &= \int_^\infty x f_(x\mid y) \, \mathrmx \\ &= \frac\int_^\infty x f_(x,y) \, \mathrmx. \end When the denominator is zero, the expression is undefined. Conditioning on a continuous random variable is not the same as conditioning on the event \ as it was in the discrete case. For a discussion, see Conditioning on an event of probability zero. Not respecting this distinction can lead to contradictory conclusions as illustrated by the Borel-Kolmogorov paradox.


L2 random variables

All random variables in this section are assumed to be in L^2, that is
square integrable In mathematics, a square-integrable function, also called a quadratically integrable function or L^2 function or square-summable function, is a real- or complex-valued measurable function for which the integral of the square of the absolute value ...
. In its full generality, conditional expectation is developed without this assumption, see below under Conditional expectation with respect to a sub-''σ''-algebra. The L^2 theory is, however, considered more intuitive and admits important generalizations. In the context of L^2 random variables, conditional expectation is also called regression. In what follows let (\Omega, \mathcal, P) be a probability space, and X: \Omega \to \mathbb in L^2 with mean \mu_X and
variance In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation (SD) is obtained as the square root of the variance. Variance is a measure of dispersion ...
\sigma_X^2. The expectation \mu_X minimizes the
mean squared error In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference betwee ...
: : \min_ \operatorname\left((X - x)^2\right) = \operatorname\left((X - \mu_X)^2\right) = \sigma_X^2. The conditional expectation of is defined analogously, except instead of a single number \mu_X, the result will be a function e_X(y). Let Y: \Omega \to \mathbb^n be a
random vector In probability, and statistics, a multivariate random variable or random vector is a list or vector of mathematical variables each of whose value is unknown, either because the value has not yet occurred or because there is imperfect knowledge ...
. The conditional expectation e_X: \mathbb^n \to \mathbb is a measurable function such that : \min_ \operatorname\left((X - g(Y))^2\right) = \operatorname\left((X - e_X(Y))^2\right). Note that unlike \mu_X, the conditional expectation e_X is not generally unique: there may be multiple minimizers of the mean squared error.


Uniqueness

Example 1: Consider the case where is the constant random variable that is always 1. Then the mean squared error is minimized by any function of the form : e_X(y) = \begin \mu_X & \text y = 1, \\ \text & \text \end Example 2: Consider the case where is the 2-dimensional random vector (X, 2X). Then clearly :\operatorname(X \mid Y) = X but in terms of functions it can be expressed as e_X(y_1, y_2) = 3y_1-y_2 or e'_X(y_1, y_2) = y_2 - y_1 or infinitely many other ways. In the context of
linear regression In statistics, linear regression is a statistical model, model that estimates the relationship between a Scalar (mathematics), scalar response (dependent variable) and one or more explanatory variables (regressor or independent variable). A mode ...
, this lack of uniqueness is called
multicollinearity In statistics, multicollinearity or collinearity is a situation where the predictors in a regression model are linearly dependent. Perfect multicollinearity refers to a situation where the predictive variables have an ''exact'' linear rela ...
. Conditional expectation is unique up to a set of measure zero in \mathbb^n. The measure used is the
pushforward measure In measure theory, a pushforward measure (also known as push forward, push-forward or image measure) is obtained by transferring ("pushing forward") a measure from one measurable space to another using a measurable function. Definition Given mea ...
induced by . In the first example, the pushforward measure is a
Dirac distribution In mathematical analysis, the Dirac delta function (or distribution), also known as the unit impulse, is a generalized function on the real numbers, whose value is zero everywhere except at zero, and whose integral over the entire real line ...
at 1. In the second it is concentrated on the "diagonal" \, so that any set not intersecting it has measure 0.


Existence

The existence of a minimizer for \min_g \operatorname\left((X - g(Y))^2\right) is non-trivial. It can be shown that : M := \ = L^2(\Omega, \sigma(Y)) is a closed subspace of the Hilbert space L^2(\Omega). By the
Hilbert projection theorem In mathematics, the Hilbert projection theorem is a famous result of convex analysis that says that for every vector x in a Hilbert space In mathematics, a Hilbert space is a real number, real or complex number, complex inner product space tha ...
, the necessary and sufficient condition for e_X to be a minimizer is that for all f(Y) in we have : \langle X - e_X(Y), f(Y) \rangle = 0. In words, this equation says that the residual X - e_X(Y) is orthogonal to the space of all functions of . This orthogonality condition, applied to the
indicator function In mathematics, an indicator function or a characteristic function of a subset of a set is a function that maps elements of the subset to one, and all other elements to zero. That is, if is a subset of some set , then the indicator functio ...
s f(Y) = 1_, is used below to extend conditional expectation to the case that and are not necessarily in L^2.


Connections to regression

The conditional expectation is often approximated in
applied mathematics Applied mathematics is the application of mathematics, mathematical methods by different fields such as physics, engineering, medicine, biology, finance, business, computer science, and Industrial sector, industry. Thus, applied mathematics is a ...
and
statistics Statistics (from German language, German: ', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a s ...
due to the difficulties in analytically calculating it, and for interpolation. The Hilbert subspace : M = \ defined above is replaced with subsets thereof by restricting the functional form of , rather than allowing any measurable function. Examples of this are decision tree regression when is required to be a
simple function In the mathematical field of real analysis, a simple function is a real (or complex)-valued function over a subset of the real line, similar to a step function. Simple functions are sufficiently "nice" that using them makes mathematical reas ...
,
linear regression In statistics, linear regression is a statistical model, model that estimates the relationship between a Scalar (mathematics), scalar response (dependent variable) and one or more explanatory variables (regressor or independent variable). A mode ...
when is required to be
affine Affine may describe any of various topics concerned with connections or affinities. It may refer to: * Affine, a Affinity_(law)#Terminology, relative by marriage in law and anthropology * Affine cipher, a special case of the more general substi ...
, etc. These generalizations of conditional expectation come at the cost of many of its properties no longer holding. For example, let be the space of all linear functions of and let \mathcal_ denote this generalized conditional expectation/L^2 projection. If M does not contain the
constant function In mathematics, a constant function is a function whose (output) value is the same for every input value. Basic properties As a real-valued function of a real-valued argument, a constant function has the general form or just For example, ...
s, the tower property \operatorname(\mathcal_M(X)) = \operatorname(X) will not hold. An important special case is when and are jointly normally distributed. In this case it can be shown that the conditional expectation is equivalent to linear regression: : e_X(Y) = \alpha_0 + \sum_i \alpha_i Y_i for coefficients \_ described in Multivariate normal distribution#Conditional distributions.


Conditional expectation with respect to a sub-''σ''-algebra

Consider the following: * (\Omega, \mathcal, P) is a
probability space In probability theory, a probability space or a probability triple (\Omega, \mathcal, P) is a mathematical construct that provides a formal model of a random process or "experiment". For example, one can define a probability space which models ...
. * X\colon\Omega \to \mathbb^n is a
random variable A random variable (also called random quantity, aleatory variable, or stochastic variable) is a Mathematics, mathematical formalization of a quantity or object which depends on randomness, random events. The term 'random variable' in its mathema ...
on that probability space with finite expectation. * \mathcal \subseteq \mathcal is a sub- ''σ''-algebra of \mathcal. Since \mathcal is a sub \sigma-algebra of \mathcal, the function X\colon\Omega \to \mathbb^n is usually not \mathcal-measurable, thus the existence of the integrals of the form \int_H X \,dP, _\mathcal, where H\in\mathcal and P, _\mathcal is the restriction of P to \mathcal, cannot be stated in general. However, the local averages \int_H X\,dP can be recovered in (\Omega, \mathcal, P, _\mathcal) with the help of the conditional expectation. A conditional expectation of ''X'' given \mathcal, denoted as \operatorname(X\mid\mathcal), is any \mathcal-
measurable function In mathematics, and in particular measure theory, a measurable function is a function between the underlying sets of two measurable spaces that preserves the structure of the spaces: the preimage of any measurable set is measurable. This is in ...
\Omega \to \mathbb^n which satisfies: : \int_H\operatorname(X \mid \mathcal)\,\mathrmP = \int_H X \,\mathrmP for each H \in \mathcal. As noted in the L^2 discussion, this condition is equivalent to saying that the residual X - \operatorname(X \mid \mathcal) is orthogonal to the indicator functions 1_H: : \langle X - \operatorname(X \mid \mathcal), 1_H \rangle = 0


Existence

The existence of \operatorname(X\mid\mathcal) can be established by noting that \mu^X\colon F \mapsto \int_F X \, \mathrmP for F \in \mathcal is a finite measure on (\Omega, \mathcal) that is
absolutely continuous In calculus and real analysis, absolute continuity is a smoothness property of functions that is stronger than continuity and uniform continuity. The notion of absolute continuity allows one to obtain generalizations of the relationship betwe ...
with respect to P. If h is the natural injection from \mathcal to \mathcal, then \mu^X \circ h = \mu^X, _\mathcal is the restriction of \mu^X to \mathcal and P \circ h = P, _\mathcal is the restriction of P to \mathcal. Furthermore, \mu^X \circ h is absolutely continuous with respect to P \circ h, because the condition :P \circ h (H) = 0 \iff P(h(H)) = 0 implies :\mu^X(h(H)) = 0 \iff \mu^X \circ h(H) = 0. Thus, we have :\operatorname(X\mid\mathcal) = \frac = \frac, where the derivatives are Radon–Nikodym derivatives of measures.


Conditional expectation with respect to a random variable

Consider, in addition to the above, * A
measurable space In mathematics, a measurable space or Borel space is a basic object in measure theory. It consists of a set and a σ-algebra, which defines the subsets that will be measured. It captures and generalises intuitive notions such as length, area, an ...
(U, \Sigma), and * A random variable Y\colon\Omega \to U. The conditional expectation of given is defined by applying the above construction on the ''σ''-algebra generated by : :\operatorname \mid Y:= \operatorname \mid\sigma(Y) By the Doob–Dynkin lemma, there exists a function e_X \colon U \to \mathbb^n such that :\operatorname \mid Y= e_X(Y).


Discussion

* This is not a constructive definition; we are merely given the required property that a conditional expectation must satisfy. ** The definition of \operatorname(X \mid \mathcal) may resemble that of \operatorname(X \mid H) for an event H but these are very different objects. The former is a \mathcal-measurable function \Omega \to \mathbb^n, while the latter is an element of \mathbb^n and \operatorname(X \mid H)\ P(H)= \int_H X \,\mathrmP= \int_H \operatorname (X\mid\mathcal)\,\mathrmP for H\in\mathcal. ** Uniqueness can be shown to be almost sure: that is, versions of the same conditional expectation will only differ on a set of probability zero. *** Often, one would like to think of \operatorname(X \mid \mathcal) as a measure on \Omega for fixed H. For example, it is extremely useful to claim that \sum_i\operatorname(X_i \mid \mathcal) is additive for almost all H. However, this does not immediately follow because each \operatorname(X_i \mid \mathcal) may have a different null set. Because countable unions of null sets are null sets, for a countable set of X_i, one can choose "versions" of each \operatorname(X_i \mid \mathcal) with aligned null sets as to maintain additivity for almost all H. However, to align the "null sets of dysfunction" of \operatorname(X_i \mid \mathcal) over all possible X_i, and thus treat \operatorname(X \mid \mathcal = H) as an almost surely unique measure over \Omega (a "regular probability measure"), we need further regularity conditions. Intuitively, to do this, we need to be able to approximate all possible X_i with a countable set of them. This directly corresponds to the conditions for creating a regular probability measure, which are separability and completeness. * The ''σ''-algebra \mathcal controls the "granularity" of the conditioning. A conditional expectation E(X\mid\mathcal) over a finer (larger) ''σ''-algebra \mathcal retains information about the probabilities of a larger class of events. A conditional expectation over a coarser (smaller) ''σ''-algebra averages over more events.


Conditional probability

For a Borel subset in \mathcal(\mathbb^n), one can consider the collection of random variables : \kappa_\mathcal(\omega, B) := \operatorname(1_, \mathcal)(\omega). It can be shown that they form a
Markov kernel In probability theory, a Markov kernel (also known as a stochastic kernel or probability kernel) is a map that in the general theory of Markov processes plays the role that the transition matrix does in the theory of Markov processes with a finit ...
, that is, for almost all \omega, \kappa_\mathcal(\omega, -) is a probability measure. The Law of the unconscious statistician is then : \operatorname (X)\mid\mathcal= \int f(x) \kappa_\mathcal(-, \mathrmx), This shows that conditional expectations are, like their unconditional counterparts, integrations, against a conditional measure.


General Definition

In full generality, consider: * A probability space (\Omega,\mathcal,P). * A
Banach space In mathematics, more specifically in functional analysis, a Banach space (, ) is a complete normed vector space. Thus, a Banach space is a vector space with a metric that allows the computation of vector length and distance between vectors and ...
(E,\, \cdot\, _E). * A Bochner integrable random variable X:\Omega\to E. * A sub-''σ''-algebra \mathcal\subseteq \mathcal. The conditional expectation of X given \mathcal is the up to a P-nullset unique and integrable E-valued \mathcal-measurable random variable \operatorname(X \mid \mathcal) satisfying :\int_H \operatorname(X \mid \mathcal) \,\mathrmP = \int_H X \,\mathrmP for all H \in \mathcal. In this setting the conditional expectation is sometimes also denoted in operator notation as \operatorname^\mathcalX.


Basic properties

All the following formulas are to be understood in an almost sure sense. * Pulling out independent factors: ** If X is
independent Independent or Independents may refer to: Arts, entertainment, and media Artist groups * Independents (artist group), a group of modernist painters based in Pennsylvania, United States * Independentes (English: Independents), a Portuguese artist ...
of \mathcal, then E(X\mid\mathcal) = E(X). Let B \in \mathcal. Then X is independent of 1_B, so we get that :\int_B X\,dP = E(X1_B) = E(X)E(1_B) = E(X)P(B) = \int_B E(X)\,dP. Thus the definition of conditional expectation is satisfied by the constant random variable E(X), as desired. \square ** If X is independent of \sigma(Y, \mathcal), then E(XY\mid \mathcal) = E(X) \, E(Y\mid\mathcal). Note that this is not necessarily the case if X is only independent of \mathcal and of Y. ** If X,Y are independent, \mathcal,\mathcal are independent, X is independent of \mathcal and Y is independent of \mathcal, then E(E(XY\mid\mathcal)\mid\mathcal) = E(X) E(Y) = E(E(XY\mid\mathcal)\mid\mathcal). * Stability: ** If X is \mathcal-measurable, then E(X\mid\mathcal) = X. For each H\in \mathcal we have \int_H E(X\mid\mathcal) \, dP = \int_H X \, dP, or equivalently : \int_H \big( E(X\mid\mathcal) - X \big) \, dP = 0 Since this is true for each H \in \mathcal, and both E(X\mid\mathcal) and X are \mathcal-measurable (the former property holds by definition; the latter property is key here), from this one can show : \int_H \big, E(X\mid\mathcal) - X \big, \, dP = 0 And this implies E(X\mid\mathcal) = X almost everywhere. \square ** In particular, for sub-''σ''-algebras \mathcal_1\subset\mathcal_2 \subset\mathcal we have E(E(X\mid\mathcal_1)\mid\mathcal_2) = E(X\mid\mathcal_1). (Note this is different from the tower property below.) ** If ''Z'' is a random variable, then \operatorname(f(Z) \mid Z)=f(Z). In its simplest form, this says \operatorname(Z \mid Z)=Z. * Pulling out known factors: ** If X is \mathcal-measurable, then E(XY\mid\mathcal) = X \, E(Y\mid\mathcal). All random variables here are assumed without loss of generality to be non-negative. The general case can be treated with X = X^+ - X^-. Fix A \in \mathcal and let X = 1_A. Then for any H \in \mathcal :\int_H E(1_A Y \mid \mathcal) \, dP = \int_H 1_A Y \, dP = \int_ Y \, dP = \int_ E(Y\mid\mathcal) \, dP = \int_H 1_A E(Y \mid \mathcal) \, dP Hence E(1_A Y \mid \mathcal) = 1_A E(Y\mid\mathcal) almost everywhere. Any simple function is a finite linear combination of indicator functions. By linearity the above property holds for simple functions: if X_n is a simple function then E(X_n Y \mid \mathcal) = X_n \, E(Y\mid \mathcal). Now let X be \mathcal-measurable. Then there exists a sequence of simple functions \_ converging monotonically (here meaning X_n \leq X_) and pointwise to X. Consequently, for Y \geq 0 , the sequence \_ converges monotonically and pointwise to X Y . Also, since E(Y\mid\mathcal) \geq 0, the sequence \_ converges monotonically and pointwise to X \, E(Y\mid\mathcal) Combining the special case proved for simple functions, the definition of conditional expectation, and deploying the monotone convergence theorem: : \int_H X \, E(Y\mid\mathcal) \, dP = \int_H \lim_ X_n \, E(Y\mid\mathcal) \, dP = \lim_ \int_H X_n E(Y\mid\mathcal) \, dP = \lim_ \int_H E(X_n Y\mid\mathcal) \, dP = \lim_ \int_H X_n Y \, dP = \int_H \lim_ X_n Y \, dP = \int_H XY \, dP = \int_H E(XY\mid\mathcal) \, dP This holds for all H\in \mathcal, whence X \, E(Y\mid\mathcal) = E(XY\mid\mathcal) almost everywhere. \square ** If ''Z'' is a random variable, then \operatorname(f(Z) Y \mid Z)=f(Z)\operatorname(Y \mid Z). *
Law of total expectation The proposition in probability theory known as the law of total expectation, the law of iterated expectations (LIE), Adam's law, the tower rule, and the smoothing property of conditional expectation, among other names, states that if X is a random ...
: E(E(X \mid \mathcal)) = E(X). * Tower property: ** For sub-''σ''-algebras \mathcal_1\subset\mathcal_2 \subset\mathcal we have E(E(X\mid\mathcal_2)\mid\mathcal_1) = E(X\mid\mathcal_1). *** A special case \mathcal_1=\ recovers the Law of total expectation: E(E(X\mid\mathcal_2) ) = E(X). *** A special case is when ''Z'' is a \mathcal-measurable random variable. Then \sigma(Z) \subset \mathcal and thus E(E(X \mid \mathcal) \mid Z) = E(X \mid Z). *** Doob martingale property: the above with Z = E(X \mid \mathcal) (which is \mathcal-measurable), and using also \operatorname(Z \mid Z)=Z, gives E(X \mid E(X \mid \mathcal)) = E(X \mid \mathcal). ** For random variables X,Y we have E(E(X\mid Y)\mid f(Y)) = E(X\mid f(Y)). ** For random variables X,Y,Z we have E(E(X\mid Y,Z)\mid Y) = E(X\mid Y). * Linearity: we have E(X_1 + X_2 \mid \mathcal) = E(X_1 \mid \mathcal) + E(X_2 \mid \mathcal) and E(a X \mid \mathcal) = a\,E(X \mid \mathcal) for a\in\R. * Positivity: If X \ge 0 then E(X \mid \mathcal) \ge 0. * Monotonicity: If X_1 \le X_2 then E(X_1 \mid \mathcal) \le E(X_2 \mid \mathcal). * Monotone convergence: If 0\leq X_n \uparrow X then E(X_n \mid \mathcal) \uparrow E(X \mid \mathcal). * Dominated convergence: If X_n \to X and , X_n, \le Y with Y \in L^1, then E(X_n \mid \mathcal) \to E(X \mid \mathcal). *
Fatou's lemma In mathematics, Fatou's lemma establishes an inequality (mathematics), inequality relating the Lebesgue integral of the limit superior and limit inferior, limit inferior of a sequence of function (mathematics), functions to the limit inferior of ...
: If \textstyle E(\inf_n X_n \mid \mathcal) > -\infty then \textstyle E(\liminf_ X_n \mid \mathcal) \le \liminf_ E(X_n \mid \mathcal). * Jensen's inequality: If f \colon \mathbb \rightarrow \mathbb is a
convex function In mathematics, a real-valued function is called convex if the line segment between any two distinct points on the graph of a function, graph of the function lies above or on the graph between the two points. Equivalently, a function is conve ...
, then f(E(X\mid \mathcal)) \le E(f(X)\mid\mathcal). *
Conditional variance In probability theory and statistics, a conditional variance is the variance of a random variable given the value(s) of one or more other variables. Particularly in econometrics, the conditional variance is also known as the scedastic function or s ...
: Using the conditional expectation we can define, by analogy with the definition of the
variance In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation (SD) is obtained as the square root of the variance. Variance is a measure of dispersion ...
as the mean square deviation from the average, the conditional variance ** Definition: \operatorname(X \mid \mathcal) = \operatorname\bigl( (X - \operatorname(X \mid \mathcal))^2 \mid \mathcal \bigr) **Algebraic formula for the variance: \operatorname(X \mid \mathcal) = \operatorname(X^2 \mid \mathcal) - \bigl(\operatorname(X \mid \mathcal)\bigr)^2 ** Law of total variance: \operatorname(X) = \operatorname(\operatorname(X \mid \mathcal)) + \operatorname(\operatorname(X \mid \mathcal)). * Martingale convergence: For a random variable X, that has finite expectation, we have E(X\mid\mathcal_n) \to E(X\mid\mathcal), if either \mathcal_1 \subset \mathcal_2 \subset \dotsb is an increasing series of sub-''σ''-algebras and \textstyle \mathcal = \sigma(\bigcup_^\infty \mathcal_n) or if \mathcal_1 \supset \mathcal_2 \supset \dotsb is a decreasing series of sub-''σ''-algebras and \textstyle \mathcal = \bigcap_^\infty \mathcal_n. * Conditional expectation as L^2-projection: If X,Y are in the
Hilbert space In mathematics, a Hilbert space is a real number, real or complex number, complex inner product space that is also a complete metric space with respect to the metric induced by the inner product. It generalizes the notion of Euclidean space. The ...
of square-integrable real random variables (real random variables with finite second moment) then ** for \mathcal-measurable Y, we have E(Y(X - E(X\mid\mathcal))) = 0, i.e. the conditional expectation E(X\mid\mathcal) is in the sense of the ''L''2(''P'') scalar product the
orthogonal projection In linear algebra and functional analysis, a projection is a linear transformation P from a vector space to itself (an endomorphism) such that P\circ P=P. That is, whenever P is applied twice to any vector, it gives the same result as if it we ...
from X to the
linear subspace In mathematics, the term ''linear'' is used in two distinct senses for two different properties: * linearity of a ''function (mathematics), function'' (or ''mapping (mathematics), mapping''); * linearity of a ''polynomial''. An example of a li ...
of \mathcal-measurable functions. (This allows to define and prove the existence of the conditional expectation based on the
Hilbert projection theorem In mathematics, the Hilbert projection theorem is a famous result of convex analysis that says that for every vector x in a Hilbert space In mathematics, a Hilbert space is a real number, real or complex number, complex inner product space tha ...
.) ** the mapping X \mapsto \operatorname(X\mid\mathcal) is
self-adjoint In mathematics, an element of a *-algebra is called self-adjoint if it is the same as its adjoint (i.e. a = a^*). Definition Let \mathcal be a *-algebra. An element a \in \mathcal is called self-adjoint if The set of self-adjoint elements ...
: \operatorname E(X \operatorname E(Y \mid \mathcal)) = \operatorname E\left(\operatorname E(X \mid \mathcal) \operatorname E(Y \mid \mathcal)\right) = \operatorname E(\operatorname E(X \mid \mathcal) Y) * Conditioning is a contractive projection of ''L''p spaces L^p(\Omega, \mathcal, P) \rightarrow L^p(\Omega, \mathcal, P). I.e., \operatorname\big(, \operatorname(X \mid\mathcal), ^p \big) \le \operatorname\big(, X, ^p\big) for any ''p'' ≥ 1. * Doob's conditional independence property: If X,Y are conditionally independent given Z, then P(X \in B\mid Y,Z) = P(X \in B\mid Z) (equivalently, E(1_\mid Y,Z) = E(1_ \mid Z)).


See also

* Conditioning (probability) * Disintegration theorem * Doob–Dynkin lemma * Factorization lemma *
Hidden Markov model A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or ''hidden'') Markov process (referred to as X). An HMM requires that there be an observable process Y whose outcomes depend on the outcomes of X ...
*
Joint probability distribution A joint or articulation (or articular surface) is the connection made between bones, ossicles, or other hard structures in the body which link an animal's skeletal system into a functional whole.Saladin, Ken. Anatomy & Physiology. 7th ed. McGraw- ...
* Non-commutative conditional expectation


Probability laws

* Law of total cumulance (generalizes the other three) *
Law of total expectation The proposition in probability theory known as the law of total expectation, the law of iterated expectations (LIE), Adam's law, the tower rule, and the smoothing property of conditional expectation, among other names, states that if X is a random ...
*
Law of total probability In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which can be realized via several distinct ev ...
* Law of total variance


Notes


References

*
William Feller William "Vilim" Feller (July 7, 1906 – January 14, 1970), born Vilibald Srećko Feller, was a Croatian–American mathematician specializing in probability theory. Early life and education Feller was born in Zagreb to Ida Oemichen-Perc, a Cro ...
, ''An Introduction to Probability Theory and its Applications'', vol 1, 1950, page 223 * Paul A. Meyer, ''Probability and Potentials'', Blaisdell Publishing Co., 1966, page 28 * , pages 67–69


External links

* {{DEFAULTSORT:Conditional Expectation Conditional probability Statistical theory