In MATLAB, I've written two snippets of code that compute the PDF of a multivariate normal distribution. However there's a difference in the values these two methods produce and I can't figure out why. I've narrowed the problem down to something having to do with computing the inverse of the covariance matrix.

Inaccurate code

function p = mvnpdf_inacc(X, mu, sigma)
    xc = bsxfun(@minus, X, mu);
    [n, k] = size(xc);
    twopic = (2 * pi) ^ (-k / 2);
    sqrtdetsig = sqrt(det(sigma)) ^ -1;
    c = twopic * sqrtdetsig;
    p = zeros(n, 1);
    for i = 1:n
        xci = xc(i, :);
        p(i) = c * exp(-0.5 * (xci / sigma * xci'));

Accurate code

function p = mvnpdf_acc(X, mu, sigma)
    [R, err] = cholcov(sigma, 0);

    if err
        error('%s', 'sigma is not both symmetric and positive definite');

    X0 = bsxfun(@minus, X, mu) / R;
    d = min(size(X));
    slogdet = sum(log(diag(R)));
    p = exp(-0.5 * sum(X0 .^ 2, 2) - slogdet - 0.5 * d * log(2 * pi));

Testing code

function iseq_func = test_mvnpdf(n)
    x = linspace(-2, 2, n);
    y = x;
    [X, Y] = meshgrid(x, y);
    XY = [X(:), Y(:)];
    mu = [0, 0];
    sigma = [1.0, 0.5; 0.5, 0.4];
    p_inacc = mvnpdf_inacc(XY, mu, sigma);
    p_acc = mvnpdf_acc(XY, mu, sigma);
    p_diff = abs(p_inacc - p_acc);
    iseq_func = nnz(p_diff) == 0;

I get a value of false from running iseq_func(25). What the heck is going on here? Thanks!

  • 1
    $\begingroup$ @varty because it is 'built-in' to the \$tati\$tic\$ toolbox, which is another thousand dollars or so. $\endgroup$
    – shabbychef
    Nov 17 '11 at 19:06
  • 4
    $\begingroup$ Where is the multiplication by $-1/2$ in the exponent in the first calculation? Am I just not seeing it? $\endgroup$
    – cardinal
    Nov 17 '11 at 19:10
  • 2
    $\begingroup$ I would pay attention to @cardinal's comment first. Roundoff error might account for differences beyond the 12th significant figure or so, and the product-vs-logarithmic approach could account for overflow problems when they occur, but otherwise neither can explain noticeable differences in the results. $\endgroup$
    – whuber
    Nov 17 '11 at 20:46
  • 1
    $\begingroup$ With @cardinal's correction (edited into the question it appears), I do not get any difference between the two versions. However, I had to change the cholcov to chol, because the former is in the statistics toolbox. (See mathworks.com/help/toolbox/stats/cholcov.html ). This may be the rub, however: I was testing the two implementations with a positive definite sigma. If that condition does not hold, then cholcov does something odd, which might explain inconsistencies, if they still exist. $\endgroup$
    – shabbychef
    Nov 18 '11 at 1:15
  • 1
    $\begingroup$ @cardinal Yes, the whole point of my question is that there is any difference whatsoever. I'm wondering why that is (other than just simply minor differences in floating-point calculations). max(p_diff) is something like 5e-14. Why should there be any difference at all? $\endgroup$ Nov 21 '11 at 0:48

I am guessing you tested the code using a sigma which is not positive semidefinite. The 'accurate' implementation (which I am guessing is from the statistics toolbox) is computing $y^{\top}y$ where $y = C^{-\top} \left(x - \mu\right)$ where $C$ is the output of cholcov on $\Sigma$ (sigma). Note that $y^{\top}y$ must be non-negative by design. If you feed in a $\Sigma$ which is not PD, cholcov silently returns some version of $C$ and the rest of the code proceeds (I would have made this an error, I think).

The code you wrote, however, is computing $\left(x - \mu\right)^{\top}\Sigma^{-1}\left(x - \mu\right)$. If $\Sigma$ is not PD, this quantity can be negative.

When $\Sigma$ is positive definite, cholcov returns a proper Cholesky factorization, and the results are the same (up to round off).

  • $\begingroup$ I used the matrix sigma = [1.0, 0.5; 0.5, 0.4];. $\endgroup$ Nov 20 '11 at 23:47
  • $\begingroup$ My matrix is plenty symmetric. Run sigma == sigma' in MATLAB. $\endgroup$ Nov 21 '11 at 0:52
  • $\begingroup$ @cpcloud : this is not a problem of symmetry, it is whether the matrix is positive definite. Try, for example sigma = [1.0, -1.4;-1.4, 0.2]. chol bonks, but cholcov returns some matrix (I am guessing. I do not have the statistics toolbox). $\endgroup$
    – shabbychef
    Nov 21 '11 at 17:46

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