Support x ∈ μ + span(Σ) ⊆ R Mode μ | Mean μ | |
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Notation N ( μ , Σ ) {displaystyle {mathcal {N}}({oldsymbol {mu }},,{oldsymbol {Sigma }})} PDF ( 2 π ) − k / 2 | Σ | − 1 / 2 e − 1 2 ( x − μ ) ′ Σ − 1 ( x − μ ) , {displaystyle (2pi )^{-k/2}left|{oldsymbol {Sigma }}ight|^{-1/2},e^{-{rac {1}{2}}(mathbf {x} -{oldsymbol {mu }})'{oldsymbol {Sigma }}^{-1}(mathbf {x} -{oldsymbol {mu }})},} exists only when Σ is positive-definite |
In probability theory and statistics, the multivariate normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One possible definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables each of which clusters around a mean value.
Contents
- Notation and parametrization
- Definition
- Non degenerate case
- Degenerate case
- Higher moments
- Likelihood function
- Entropy
- KullbackLeibler divergence
- Mutual Information
- Cumulative distribution function
- Interval
- Normally distributed and independent
- Two normally distributed random variables need not be jointly bivariate normal
- Correlations and independence
- Conditional distributions
- Bivariate case
- In the general case
- In the case of unit variances
- Marginal distributions
- Affine transformation
- Geometric interpretation
- Estimation of parameters
- Bayesian inference
- Multivariate normality tests
- Drawing values from the distribution
- References
Notation and parametrization
The multivariate normal distribution of a k-dimensional random vector x = [X1, X2, …, Xk] can be written in the following notation:
or to make it explicitly known that X is k-dimensional,
with k-dimensional mean vector
and
Definition
A random vector x = (X1, …, Xk)' is said to have the multivariate normal distribution if it satisfies the following equivalent conditions.
The covariance matrix is allowed to be singular (in which case the corresponding distribution has no density). This case arises frequently in statistics; for example, in the distribution of the vector of residuals in the ordinary least squares regression. Note also that the Xi are in general not independent; they can be seen as the result of applying the matrix A to a collection of independent Gaussian variables z.
Non-degenerate case
The multivariate normal distribution is said to be "non-degenerate" when the symmetric covariance matrix
where
Note that the circularly symmetric version of the complex normal distribution has a slightly different form.
Each iso-density locus—the locus of points in k-dimensional space each of which gives the same particular value of the density—is an ellipse or its higher-dimensional generalization; hence the multivariate normal is a special case of the elliptical distributions.
The descriptive statistic
In the 2-dimensional nonsingular case (k = rank(Σ) = 2), the probability density function of a vector [X Y]′ is:
where ρ is the correlation between X and Y and where
In the bivariate case, the first equivalent condition for multivariate normality can be made less restrictive: it is sufficient to verify that countably many distinct linear combinations of X and Y are normal in order to conclude that the vector [X Y]′ is bivariate normal.
The bivariate iso-density loci plotted in the x,y-plane are ellipses. As the correlation parameter ρ increases, these loci appear to be squeezed to the following line :
This is because the above expression – but without ρ being inside a signum function – is the best linear unbiased prediction of Y given a value of X.
Degenerate case
If the covariance matrix
To talk about densities meaningfully in the singular case, then, we must select a different base measure. Using the disintegration theorem we can define a restriction of Lebesgue measure to the
where
Higher moments
The kth-order moments of x are given by
where r1 + r2 + ⋯ + rN = k.
The kth-order central moments are as follows
where the sum is taken over all allocations of the set
This yields
The covariances are then determined by replacing the terms of the list
where
Likelihood function
If the mean and variance matrix are known, a suitable log likelihood function for a single observation x is
where x is a vector of real numbers (to derive this, simply take the log of the PDF). The circularly symmetric version of the complex case, where z is a vector of complex numbers, would be
i.e. with the conjugate transpose (indicated by
A similar notation is used for multiple linear regression.
Entropy
The entropy of the multivariate normal distribution is
where the bars denote the matrix determinant and k is the dimensionality of the vector space.
Kullback–Leibler divergence
The Kullback–Leibler divergence from
where
The logarithm must be taken to base e since the two terms following the logarithm are themselves base-e logarithms of expressions that are either factors of the density function or otherwise arise naturally. The equation therefore gives a result measured in nats. Dividing the entire expression above by loge 2 yields the divergence in bits.
Mutual Information
The mutual information of a distribution is a special case of the Kullback–Leibler divergence in which
where
In the bivariate case the expression for the mutual information is:
Cumulative distribution function
The notion of cumulative distribution function (cdf) in dimension 1 can be extended in two ways to the multidimensional case, based on rectangular and ellipsoidal regions.
The first way is to define the cdf
Though there is no closed form for
Another way is to define the cdf
Interval
The interval for the multivariate normal distribution yields a region consisting of those vectors x satisfying
Here
Normally distributed and independent
If X and Y are normally distributed and independent, this implies they are "jointly normally distributed", i.e., the pair (X, Y) must have multivariate normal distribution. However, a pair of jointly normally distributed variables need not be independent (would only be so if uncorrelated,
Two normally distributed random variables need not be jointly bivariate normal
The fact that two random variables X and Y both have a normal distribution does not imply that the pair (X, Y) has a joint normal distribution. A simple example is one in which X has a normal distribution with expected value 0 and variance 1, and Y = X if |X| > c and Y = −X if |X| < c, where c > 0. There are similar counterexamples for more than two random variables. In general, they sum to a mixture model.
Correlations and independence
In general, random variables may be uncorrelated but statistically dependent. But if a random vector has a multivariate normal distribution then any two or more of its components that are uncorrelated are independent. This implies that any two or more of its components that are pairwise independent are independent.
But it is not true that two random variables that are (separately, marginally) normally distributed and uncorrelated are independent. Two random variables that are normally distributed may fail to be jointly normally distributed, i.e., the vector whose components they are may fail to have a multivariate normal distribution.
Conditional distributions
If N-dimensional x is partitioned as follows
and accordingly μ and Σ are partitioned as follows
then the distribution of x1 conditional on x2 = a is multivariate normal (x1 | x2 = a) ~ N(μ, Σ) where
and covariance matrix
This matrix is the Schur complement of Σ22 in Σ. This means that to calculate the conditional covariance matrix, one inverts the overall covariance matrix, drops the rows and columns corresponding to the variables being conditioned upon, and then inverts back to get the conditional covariance matrix. Here
Note that knowing that x2 = a alters the variance, though the new variance does not depend on the specific value of a; perhaps more surprisingly, the mean is shifted by
An interesting fact derived in order to prove this result, is that the random vectors
The matrix Σ12Σ22−1 is known as the matrix of regression coefficients.
Bivariate case
In the bivariate case where x is partitioned into X1 and X2, the conditional distribution of X1 given X2 is
where
In the general case
The conditional expectation of X1 given X2 is:
Proof: the result is obtained by taking the expectation of the conditional distribution
In the case of unit variances
The conditional expectation of X1 given X2 is
and the conditional variance is
thus the conditional variance does not depend on x2.
The conditional expectation of X1 given that X2 is smaller/bigger than z is (Maddala 1983, p. 367) :
where the final ratio here is called the inverse Mills ratio.
Proof: the last two results are obtained using the result
Marginal distributions
To obtain the marginal distribution over a subset of multivariate normal random variables, one only needs to drop the irrelevant variables (the variables that one wants to marginalize out) from the mean vector and the covariance matrix. The proof for this follows from the definitions of multivariate normal distributions and linear algebra.
Example
Let x = [X1, X2, X3] be multivariate normal random variables with mean vector μ = [μ1, μ2, μ3] and covariance matrix Σ (standard parametrization for multivariate normal distributions). Then the joint distribution of x′ = [X1, X3] is multivariate normal with mean vector μ′ = [μ1, μ3] and covariance matrix
Affine transformation
If y = c + Bx is an affine transformation of
which extracts the desired elements directly.
Another corollary is that the distribution of Z = b · x, where b is a constant vector with the same number of elements as x and the dot indicates the dot product, is univariate Gaussian with
Observe how the positive-definiteness of Σ implies that the variance of the dot product must be positive.
An affine transformation of x such as 2x is not the same as the sum of two independent realisations of x.
Geometric interpretation
The equidensity contours of a non-singular multivariate normal distribution are ellipsoids (i.e. linear transformations of hyperspheres) centered at the mean. Hence the multivariate normal distribution is an example of the class of elliptical distributions. The directions of the principal axes of the ellipsoids are given by the eigenvectors of the covariance matrix Σ. The squared relative lengths of the principal axes are given by the corresponding eigenvalues.
If Σ = UΛUT = UΛ1/2(UΛ1/2)T is an eigendecomposition where the columns of U are unit eigenvectors and Λ is a diagonal matrix of the eigenvalues, then we have
Moreover, U can be chosen to be a rotation matrix, as inverting an axis does not have any effect on N(0, Λ), but inverting a column changes the sign of U's determinant. The distribution N(μ, Σ) is in effect N(0, I) scaled by Λ1/2, rotated by U and translated by μ.
Conversely, any choice of μ, full rank matrix U, and positive diagonal entries Λi yields a non-singular multivariate normal distribution. If any Λi is zero and U is square, the resulting covariance matrix UΛUT is singular. Geometrically this means that every contour ellipsoid is infinitely thin and has zero volume in n-dimensional space, as at least one of the principal axes has length of zero.
"The radius around the true mean in a bivariate normal random variable, re-written in polar coordinates (radius and angle), follows a Hoyt distribution.
Estimation of parameters
The derivation of the maximum-likelihood estimator of the covariance matrix of a multivariate normal distribution is straightforward.
In short, the probability density function (pdf) of a multivariate normal is
and the ML estimator of the covariance matrix from a sample of n observations is
which is simply the sample covariance matrix. This is a biased estimator whose expectation is
An unbiased sample covariance is
The Fisher information matrix for estimating the parameters of a multivariate normal distribution has a closed form expression. This can be used, for example, to compute the Cramér–Rao bound for parameter estimation in this setting. See Fisher information for more details.
Bayesian inference
In Bayesian statistics, the conjugate prior of the mean vector is another multivariate normal distribution, and the conjugate prior of the covariance matrix is an inverse-Wishart distribution
and that a conjugate prior has been assigned, where
where
and
Then,
where
Multivariate normality tests
Multivariate normality tests check a given set of data for similarity to the multivariate normal distribution. The null hypothesis is that the data set is similar to the normal distribution, therefore a sufficiently small p-value indicates non-normal data. Multivariate normality tests include the Cox-Small test and Smith and Jain's adaptation of the Friedman-Rafsky test.
Mardia's test is based on multivariate extensions of skewness and kurtosis measures. For a sample {x1, ..., xn} of k-dimensional vectors we compute
Under the null hypothesis of multivariate normality, the statistic A will have approximately a chi-squared distribution with 1/6⋅k(k + 1)(k + 2) degrees of freedom, and B will be approximately standard normal N(0,1).
Mardia's kurtosis statistic is skewed and converges very slowly to the limiting normal distribution. For medium size samples
Mardia's tests are affine invariant but not consistent. For example, the multivariate skewness test is not consistent against symmetric non-normal alternatives.
The BHEP test computes the norm of the difference between the empirical characteristic function and the theoretical characteristic function of the normal distribution. Calculation of the norm is performed in the L2(μ) space of square-integrable functions with respect to the Gaussian weighting function
The limiting distribution of this test statistic is a weighted sum of chi-squared random variables, however in practice it is more convenient to compute the sample quantiles using the Monte-Carlo simulations.
A detailed survey of these and other test procedures is available.
Drawing values from the distribution
A widely used method for drawing (sampling) a random vector x from the N-dimensional multivariate normal distribution with mean vector μ and covariance matrix Σ works as follows:
- Find any real matrix A such that A AT = Σ. When Σ is positive-definite, the Cholesky decomposition is typically used, and the extended form of this decomposition can always be used (as the covariance matrix may be only positive semi-definite) in both cases a suitable matrix A is obtained. An alternative is to use the matrix A = UΛ½ obtained from a spectral decomposition Σ = UΛUT of Σ. The former approach is more computationally straightforward but the matrices A change for different orderings of the elements of the random vector, while the latter approach gives matrices that are related by simple re-orderings. In theory both approaches give equally good ways of determining a suitable matrix A, but there are differences in computation time.
- Let z = (z1, …, zN)T be a vector whose components are N independent standard normal variates (which can be generated, for example, by using the Box–Muller transform).
- Let x be μ + Az. This has the desired distribution due to the affine transformation property.