Last updated: 2022-03-01

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You need to have basic familiarity with univariate normal distribution, and understand the basic property that linear combinations of normals are also normal.

Motivating example

Suppose that \(Z_1,Z_2\) are independent standard normal \(N(0,1)\) and define \(X_1=Z_1+0.1 Z_2\) and \(X_2=Z_1-0.1 Z_2\). What is the joint distribution of \(X_1,X_2\)?

We know from the basic property that \(X_1\) will be univariate normal, and that \(X_2\) will be univariate normal. However, they will not necessarily be independent because \(Z_1\) and \(Z_2\) were used to compute both. Indeed, you can see that \(X_1\) and \(X_2\) might both be expected to be close to \(Z_1\) (because the 0.1 multiplier on \(Z_2\) is “relatively small”). So when \(X_1\) is big we should expect \(X_2\) will likely be big, and when \(X_1\) is small we should expect \(X_2\) will likely small.

The following code illustrates this: the histograms illustrate both \(X_1\) and \(X_2\) are normal, and the scatterplot of \(X_1\) and \(X_2\) shows they are correlated (and the sample correlation is approximately 0.98).

Z1 = rnorm(1000)
Z2 = rnorm(1000)

X1 = Z1+0.1*Z2
X2 = Z1-0.1*Z2


Version Author Date
77d271e Matthew Stephens 2022-03-01

Version Author Date
77d271e Matthew Stephens 2022-03-01
plot(X1,X2, main="scatterplot of (X1,X2)", ylim=c(-4,4), asp=1) #asp=1 sets the scales of X1 and X2 the same

Version Author Date
77d271e Matthew Stephens 2022-03-01
[1] 0.9798486

The bivariate normal distribution

In fact the answer to the question “what is the joint distribution of \(X_1,X_2\)” is they have a “bivariate normal distribution”. Thus the scatterplot shown above shows a scatterplot of 1000 samples from a bivariate normal distribution. The prefix “bi” means 2, referring to the fact that here we are looking at 2 variables, \(X_1\) and \(X_2\). The ideas here can be extended to more variables, and the resulting distribution is called the “multivariate normal”. The bivariate normal is a special case of the multivariate normal.

Mean and Covariance Matrix

The bivariate normal distribution has 5 parameters: two means (for \(X_1\) and \(X_2\)), two variances (for \(X_1\) and \(X_2\)) and the covariance between \(X_1\) and \(X_2\). It is usual to write the mean parameter as a vector \(\mu\) and the variance and covariance parameters as a 2x2 symmetric matrix \(\Sigma\), where the diagonal elements of \(\Sigma\) contain the variances and the off-diagonal elements contain the covariance. \(\Sigma\) is called the “covariance matrix” (or sometimes the “variance-covariance matrix”).

General Construction

Suppose \(Z_1,Z_2\) are independent random variables each with a standard normal distribution \(N(0,1)\). Let \(Z\) denote the vector \((Z_1,Z_2)\), let \(A\) be any \(2 \times 2\) matrix, and \(\mu\) be any \(r\)-vector. Then the vector \(X = AZ+\mu\) has a bivariate normal distribution with mean \(\mu\) and variance-covariance matrix \(\Sigma=AA'\). (Here \(A'\) means the transpose of the matrix \(A\).) We write \(X \sim N_2(\mu,\Sigma)\).


We can redo the example above in vector and matrix notation, with \(\mu=(0,0)\) and \(A=(1,0.1),(1,-0.1)\). Here for clarity we just simulate a single sample instead of 1000:

mu = c(0,0)
A = rbind(c(1,0.1),c(1,-0.1))
     [,1] [,2]
[1,]    1  0.1
[2,]    1 -0.1
z = rnorm(2)
x = mu + A %*% z
[1,] -0.5002188
[2,] -0.7154634

It should be clear from the above that in our example the mean is \(\mu=(0,0)\). What is the covariance matrix \(\Sigma\)? We can compute it from the formula \(\Sigma = AA'\):

Sigma = A %*% t(A)
     [,1] [,2]
[1,] 1.01 0.99
[2,] 0.99 1.01

R version 4.1.0 Patched (2021-07-20 r80657)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
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 [9] fansi_0.5.0      highr_0.9        stringr_1.4.0    tools_4.1.0     
[13] xfun_0.28        utf8_1.2.2       git2r_0.29.0     jquerylib_0.1.4 
[17] htmltools_0.5.2  ellipsis_0.3.2   rprojroot_2.0.2  yaml_2.2.1      
[21] digest_0.6.28    tibble_3.1.6     lifecycle_1.0.1  crayon_1.4.2    
[25] later_1.3.0      vctrs_0.3.8      fs_1.5.0         promises_1.2.0.1
[29] glue_1.5.0       evaluate_0.14    rmarkdown_2.11   stringi_1.7.5   
[33] compiler_4.1.0   pillar_1.6.4     httpuv_1.6.3     pkgconfig_2.0.3 

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