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# Pre-requisites

You should be familiar with the multivariate normal distribution and the idea of conditional independence, particularly as illustrated by a Markov Chain.

# Overview

This vignette introduces the precision matrix of a multivariate normal. It also illustrates its key property: the zeros of the precision matrix correspond to conditional independencies of the variables.

# Definition, and statement of key property

Let $$X$$ be multivariate normal with covariance matrix $$\Sigma$$.

The precision matrix, $$\Omega$$, is simply defined to be the inverse of the covariance matrix: $\Omega := \Sigma^{-1}$.

The key property of the precision matrix is that its zeros tell you about conditional independence. Specifically: $\Omega_{ij}=0 \text{ if and only if } X_i \text{ and } X_j \text{ are conditionally independent given all other coordinates of } X.$

It may help to compare and contrast this with the analogous property of the covariance matrix: $\Sigma_{ij}=0 \text{ if and only if } X_i \text{ and } X_j \text{ are independent}.$

That is, whereas zeros of the covariance matrix tell you about independence, zeros of the precision matrix tell you about conditional independence.

# Example: A normal markov chain

Consider a Markov chain $$X_1,X_2,X_3,\dots$$ where the transitions are given by $$X_{t+1} | X_{t} \sim N(X_{t},1)$$. You might think of this Markov chain as corresponding to a type of “random walk”: given the current state, the next state is obtained by adding a random normal with mean 0 and variance 1.

The following code simulates a realization of this Markov chain, starting from an initial state $$X_1 \sim N(0,1)$$, and plots it.

set.seed(100)
sim_normal_MC=function(length=1000){
X = rep(0,length)
X[1] = rnorm(1)
for(t in 2:length){
X[t]= X[t-1] + rnorm(1)
}
return(X)
}
plot(sim_normal_MC())

Version Author Date
02d2d36 stephens999 2017-02-20
c3b365a John Blischak 2017-01-02

## The normal markov chain as a multivariate normal

If you think a little you should be able to see that the above random walk simulation is actually simulating from a 1000-dimensional multivariate normal distribution!

Why?

Well, let’s write each of the $$N(0,1)$$ variables we generate using rnorm() in that code as $$Z_1,Z_2,\dots$$. Then: $X_1 = Z_1$ $X_2 = X_1 + Z_2 = Z_1 + Z_2$ $X_3 = X_2 + Z_3 = Z_1 + Z_2 + Z_3$ etc.

So we can write $$X = AZ$$ where $$A$$ is the 1000 by 1000 matrix $A = \begin{pmatrix} 1 & 0 & 0 & 0 & \dots \\ 1 & 1 & 0 & 0 & \dots \\ 1 & 1 & 1 & 0 & \dots \\ \dots \end{pmatrix}.$

Let’s take a look at what the covariance matrix Sigma looks like. (We get a good idea from just looking at the top left corner of the matrix what the pattern is)

A = matrix(0,nrow=1000,ncol=1000)
for(i in 1:1000){
A[i,]=c(rep(1,i),rep(0,1000-i))
}
Sigma = A %*% t(A)
Sigma[1:10,1:10]
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    1    1    1    1    1    1    1    1    1     1
[2,]    1    2    2    2    2    2    2    2    2     2
[3,]    1    2    3    3    3    3    3    3    3     3
[4,]    1    2    3    4    4    4    4    4    4     4
[5,]    1    2    3    4    5    5    5    5    5     5
[6,]    1    2    3    4    5    6    6    6    6     6
[7,]    1    2    3    4    5    6    7    7    7     7
[8,]    1    2    3    4    5    6    7    8    8     8
[9,]    1    2    3    4    5    6    7    8    9     9
[10,]    1    2    3    4    5    6    7    8    9    10

Now let us examine the precision matrix, $$\Omega$$, which recall is the inverse of $$\Sigma$$. Again we just show the top left corner of the precision matrix here.

Omega = chol2inv(chol(Sigma))
Omega[1:10,1:10]
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    2   -1    0    0    0    0    0    0    0     0
[2,]   -1    2   -1    0    0    0    0    0    0     0
[3,]    0   -1    2   -1    0    0    0    0    0     0
[4,]    0    0   -1    2   -1    0    0    0    0     0
[5,]    0    0    0   -1    2   -1    0    0    0     0
[6,]    0    0    0    0   -1    2   -1    0    0     0
[7,]    0    0    0    0    0   -1    2   -1    0     0
[8,]    0    0    0    0    0    0   -1    2   -1     0
[9,]    0    0    0    0    0    0    0   -1    2    -1
[10,]    0    0    0    0    0    0    0    0   -1     2

Notice all the 0s in the precision matrix. This is because of the conditional independencies that occur in a Markov chain. In a Markov chain (any Markov chain) the conditional distribution of $$X_t$$ given the other $$X_s$$ ($$s \neq t$$) depends only on its neighbors $$X_{t-1}$$ and $$X_{t+1}$$. That is, $$X_{t}$$ is conditionally independent of all other $$X_s$$ given $$X_{t-1}$$ and $$X_{t+1}$$. This is exactly what we are seeing in the precision matrix above: the non-zero elements of the $$t$$th row are at coordinates $$t-1,t$$ and $$t+1$$.

# Addendum: interpretation of $$\Omega$$ in terms of conditional mean of $$X_i$$

The following fact is also useful, both in practice and for intuition.

Suppose $$X \sim N_r(0,\Omega^{-1})$$, where the subscript $$r$$ indicates that $$X$$ is $$r$$-variate.

Let $$Y_1$$ denote the first coordinate of $$X$$ and $$Y_2$$ denote the remaining coordinates (so $$Y_2:= (X_2,\dots,X_r)$$). Further let $$\Omega_{12}$$ denote the $$1 \times (r-1)$$ sub matrix of $$\Omega$$ that consists of row 1 and columns 2 to r.

The conditional distribution of $$Y_1 | Y_2$$ is (univariate) normal, with mean $E(Y_1 | Y_2) = - (1/\Omega_{11}) \Omega_{12} Y_2$
and variance $$1/\Omega_{11}$$.

Of course there is nothing special about $$X_1$$: a similar result applies for any $$X_p$$. You just have to replace $$\Omega_{11}$$ with $$\Omega_{pp}$$ and define $$\Omega_{12}$$ to be the $$p$$th row of $$\Omega$$ with all columns except $$p$$.

## Application

An application of this is imputation of missing values: suppose one of the $$X$$ values is missing, say $$X_p$$ is missing, but you know the covariance matrix and all the other $$X$$ values. Then you could impute $$X_p$$ by its conditional mean, which is a simple linear combination of the other values that can be read directly off the $$p$$th row of the precision matrix. This idea is the essence of Kriging.

## Example

Consider the Markov chain above. The conditional distribution of $$X_1$$ given all other $$X$$ values is given by $X_1 | X_2,X_3,\dots \sim N(X_2/2, 1/2).$

And the conditional distribution of $$X_2$$ given all other $$X$$ values is $X_2 | X_1,X_3, \dots \sim N((X_1+X_3)/2, 1/2).$ Similarly for $$X_p$$ for $$p>2$$. The intuition is that, if one wanted to guess what the value of $$X_p$$ were given all other $$X$$s, the best guess would be the average of its neighbours.

sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[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):
[1] workflowr_1.2.0 Rcpp_1.0.0      digest_0.6.18   rprojroot_1.3-2
[5] backports_1.1.3 git2r_0.24.0    magrittr_1.5    evaluate_0.12
[9] stringi_1.2.4   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11
[13] tools_3.5.2     stringr_1.3.1   glue_1.3.0      xfun_0.4
[17] yaml_2.2.0      compiler_3.5.2  htmltools_0.3.6 knitr_1.21     

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