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';
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); end 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 = 3.9253e-13
sum( H(:,1).*H(:,3).*rho )
ans = 2.2161e-15
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 = 1.0000 -0.0000 0.0000 -0.0000 0.0000 0.0000 -0.0000 1.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 -0.0000 2.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 0.0000 6.0000 -0.0000 -0.0000 0.0000 0.0000 -0.0000 -0.0000 24.0000 0.0000 0.0000 -0.0000 -0.0000 -0.0000 0.0000 120.0000
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 = 3.0802 1.5401 0.3850 0.0642 0.0080 0.0008
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 = 49.6444 105.8423 56.9519 39.1043 10.0267 5.0134
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 = -20.6256 -41.6303 -33.4229 -16.5147 -4.7469 -0.6523
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 = 2.6133 2.6880 0.4441 0.4504 0.1648 0.0944
Note that both curves in the graph changed, because the parameterization and the approximation are both different.
 D. Xiu, Numerical Methods for Stochastic Computations, Princeton University Press, 2010.