Generalized Polynomial Chaos

Toby Driscoll, 16th December 2011


(Chebfun example stats/GeneralizedPolynomialChaos.m)

gPC is a powerful way to express stochastic quantities with spectral accuracy. We demonstrate the technique in one dimension.

LW = 'linewidth'; FS = 'fontsize'; MS = 'markersize';

Strong approximation

When the density of a random variable Y is known explicitly, and can be reparameterized in terms of a standard random variable Z, then a polynomial approximation in Z can reproduce Y accurately. The gPC method expresses the approximation using orthogonal polynomials based on the density of Z, so that the approximation is a very simple least-squares (i.e., Fourier) projection.

For example, suppose Y is a lognormal variable (that is, log(Y) is normal with mean mu and variance sigma^2). If Z is a standard normal variable, then Y = exp(mu+sigma*Z). A standard gPC approximation will use Hermite polynomials in Z as a basis, as they are orthogonal when weighted by the Gaussian density of Z. Here, we build those using a three-term recurrence on a truncated domain. We are going to truncate the infinite domain to 10 standard deviations.

z = chebfun(@(z) z,[-10 10]);
H = [ 1,z ];  N = 5;
for n = 1:N-1
    H(:,n+2) = z.*H(:,n+1) - n*H(:,n);
plot(H(:,1:4),LW,2), title('Hermite polynomials')

The plot shows that the polynomials are not uniformly normalized, but that won't be a problem. We can verify the orthogonality in a few cases:

rho = exp(-z.^2/2);  rho = rho/sum(rho);  % Gaussian density
sum( H(:,2).*H(:,5).*rho )
ans =
sum( H(:,1).*H(:,3).*rho )
ans =

To check things more globally, we can use the square root of the density rho to recast everything into the L2 norm.

w = exp(-z.^2/4);  w = w/sqrt(sum(w.^2));  % square root of weight
HW = repmat(w,[1 N+1]).*H;                 % multiply each column by w
G = HW'*HW    % Gram matrix of mutual inner products
G =
   1.0e+02 *
  Columns 1 through 3
   0.010000000000000  -0.000000000000000   0.000000000000000
  -0.000000000000000   0.010000000000000   0.000000000000000
   0.000000000000000  -0.000000000000000   0.020000000000000
  -0.000000000000001   0.000000000000000   0.000000000000001
   0.000000000000003   0.000000000000001  -0.000000000000002
   0.000000000000036  -0.000000000000002  -0.000000000000018
  Columns 4 through 6
  -0.000000000000001   0.000000000000003   0.000000000000036
   0.000000000000000   0.000000000000001  -0.000000000000002
   0.000000000000001  -0.000000000000002  -0.000000000000018
   0.059999999999999  -0.000000000000003  -0.000000000000010
  -0.000000000000003   0.240000000000009   0.000000000000039
  -0.000000000000010   0.000000000000039   1.200000000000125

The Gram matrix shows why it's so easy to use this basis for least-squares approximation. If we accept that G is numerically diagonal, then its inversion in the normal equations is trivial. Returning to our lognormal variable Y, its approximation has coefficients given by:

mu = 1; sigma = 0.5;  y = exp(mu+sigma*z);
rhs = (repmat(rho,[1 N+1]).*H)' * y;  % inner products of y and H_n
c = rhs ./ diag(G)                    % solve the normal equations
c =

The rapid decrease in the coefficients reflects the spectral accuracy. A plot shows how the weight function strongly emphasizes values of Z near 0 at the expense of the ends:

clf, plot([y,H*c],LW,2), title('Weighted least squares for lognormal density')
xlabel('Z'), ylabel('Y(Z)')

In Chebfun, we can avoid all the discussion and use of orthogonal polynomials and go straight to the least-squares solution. We'll use the Chebyshev polynomials as the basis; though they aren't orthogonal in this inner product, they're (barely!) well-conditioned enough to do the job. The main trick is to again recast everything in the L2 norm, then use Chebfun's built in least-squares solutions.

T = chebpoly(0:N,[-10 10]);
WT = repmat(w,[1 N+1]).*T;  wy = w.*y;
c = WT \ wy
hold on, plot(T*c,'r',LW,2)
title('Weighted least squares using Chebyshev basis')
c =
   1.0e+02 *

Weak approximation

In practice, the more common situation is that the explicit parameterization Y(Z) is unknown, but the distribution FY(y)=P[Y<=y] is known. In this case, the trick is to reparameterize both Y and Z in terms of a variable in their common range [0,1]. Specifically, both FY(Y) and FZ(Z) are uniformly distributed and can be called a new variable U. Solving, we get Y=FY^(-1)( FZ(Z) ), a quantity that we can approximate as before.

For example, let Y be given by an exponential distribution on [0,inf], which we truncate for simplicity. All we would know, in principle, is the distribution FY(y) = 1-exp(-y), which Chebfun can invert.

FY = chebfun(@(y) 1-exp(-y),[0 8]);

Let's again use the Hermite weight in Z to approximate Y.

z = chebfun(@(z)z, [0 8] );
w = exp(-z.^2/4) / sqrt(sum(exp(-z.^2/2)));  % sqrt of density
FZ = cumsum( w.^2 );                         % distribution of Z

Because function composition can be very sensitive to roundoff, we have to guarantee that the ranges of the distributions are exactly right.

FY = (FY-FY(0))/(FY(8)-FY(0));
FZ = (FZ-FZ(0))/(FZ(8)-FZ(0));
FYinv = inv2(FY);  % invert
y = FYinv(FZ);     % compose

Now that we have an expression for Y as parameterized by Z, we can proceed as before with a least-squares approximation.

T = chebpoly(0:N,[0 8]);
WT = repmat(w,[1 N+1]).*T;  wy = w.*y;
c = WT \ wy
clf, plot([y,T*c],LW,2)
title('Weighted least squares approximation of exponential distribution')
xlabel('Z'), ylabel('Y(Z)')
legend('exact','LS approximation','Location','southwest')
c =

If we decide not to deemphasize the right end so much, we could use a weight function that is only inverse square rather than exponential.

w = 1./(1+z);      % sqrt of density
FZ = cumsum( w.^2 );
FZ = (FZ-FZ(0))/(FZ(8)-FZ(0));  % normalize exactly
y = FYinv(FZ);
WT = repmat(w,[1 N+1]).*T;  wy = w.*y;
c = WT \ wy
clf, plot([y,T*c],LW,2)
title('Another least squares approximation of exponential distribution')
xlabel('Z'), ylabel('Y(Z)')
legend('exact','LS approximation','Location','southwest')
c =

Note that both curves in the graph changed, because the parameterization and the approximation are both different.


[1] D. Xiu, Numerical Methods for Stochastic Computations, Princeton University Press, 2010.