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+## Author: Paul Kienzle <pkienzle@users.sf.net>
+## This program is granted to the public domain.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{y} =} mvnpdf (@var{x})
+## @deftypefnx{Function File} {@var{y} =} mvnpdf (@var{x}, @var{mu})
+## @deftypefnx{Function File} {@var{y} =} mvnpdf (@var{x}, @var{mu}, @var{sigma})
+## Compute multivariate normal pdf for @var{x} given mean @var{mu} and covariance matrix 
+## @var{sigma}.  The dimension of @var{x} is @var{d} x @var{p}, @var{mu} is
+## @var{1} x @var{p} and @var{sigma} is @var{p} x @var{p}. The normal pdf is
+## defined as
+##
+## @example
+## @iftex
+## @tex
+## $$ 1/y^2 = (2 pi)^p |\Sigma| \exp \{ (x-\mu)^T \Sigma^{-1} (x-\mu) \} $$
+## @end tex
+## @end iftex
+## @ifnottex
+## 1/@var{y}^2 = (2 pi)^@var{p} |@var{Sigma}| exp @{ (@var{x}-@var{mu})' inv(@var{Sigma})@
+## (@var{x}-@var{mu}) @}
+## @end ifnottex
+## @end example
+##
+## @strong{References}
+## 
+## NIST Engineering Statistics Handbook 6.5.4.2
+## http://www.itl.nist.gov/div898/handbook/pmc/section5/pmc542.htm
+##
+## @strong{Algorithm}
+##
+## Using Cholesky factorization on the positive definite covariance matrix:
+##
+## @example
+## @var{r} = chol (@var{sigma});
+## @end example
+##
+## where @var{r}'*@var{r} = @var{sigma}. Being upper triangular, the determinant
+## of @var{r} is  trivially the product of the diagonal, and the determinant of
+## @var{sigma} is the square of this:
+##
+## @example
+## @var{det} = prod (diag (@var{r}))^2;
+## @end example
+##
+## The formula asks for the square root of the determinant, so no need to 
+## square it.
+##
+## The exponential argument @var{A} = @var{x}' * inv (@var{sigma}) * @var{x}
+##
+## @example
+## @var{A} = @var{x}' * inv (@var{sigma}) * @var{x}
+##   = @var{x}' * inv (@var{r}' * @var{r}) * @var{x}
+##   = @var{x}' * inv (@var{r}) * inv(@var{r}') * @var{x}
+## @end example
+##
+## Given that inv (@var{r}') == inv(@var{r})', at least in theory if not numerically,
+##
+## @example
+## @var{A}  = (@var{x}' / @var{r}) * (@var{x}'/@var{r})' = sumsq (@var{x}'/@var{r})
+## @end example
+##
+## The interface takes the parameters to the multivariate normal in columns rather than 
+## rows, so we are actually dealing with the transpose:
+##
+## @example
+## @var{A} = sumsq (@var{x}/r)
+## @end example
+##
+## and the final result is:
+##
+## @example
+## @var{r} = chol (@var{sigma})
+## @var{y} = (2*pi)^(-@var{p}/2) * exp (-sumsq ((@var{x}-@var{mu})/@var{r}, 2)/2) / prod (diag (@var{r}))
+## @end example
+##
+## @seealso{mvncdf, mvnrnd}
+## @end deftypefn
+
+function pdf = mvnpdf (x, mu = 0, sigma = 1)
+  ## Check input
+  if (!ismatrix (x))
+    error ("mvnpdf: first input must be a matrix");
+  endif
+  
+  if (!isvector (mu) && !isscalar (mu))
+    error ("mvnpdf: second input must be a real scalar or vector");
+  endif
+  
+  if (!ismatrix (sigma) || !issquare (sigma))
+    error ("mvnpdf: third input must be a square matrix");
+  endif
+  
+  [ps, ps] = size (sigma);
+  [d, p] = size (x);
+  if (p != ps)
+    error ("mvnpdf: dimensions of data and covariance matrix does not match");
+  endif
+  
+  if (numel (mu) != p && numel (mu) != 1)
+    error ("mvnpdf: dimensions of data does not match dimensions of mean value");
+  endif
+
+  mu = mu (:).';
+  if (all (size (mu) == [1, p]))
+    mu = repmat (mu, [d, 1]);
+  endif
+  
+  if (nargin < 3)
+    pdf = (2*pi)^(-p/2) * exp (-sumsq (x-mu, 2)/2);
+  else
+    r = chol (sigma);
+    pdf = (2*pi)^(-p/2) * exp (-sumsq ((x-mu)/r, 2)/2) / prod (diag (r));
+  endif
+endfunction
+
+%!demo
+%! mu = [0, 0];
+%! sigma = [1, 0.1; 0.1, 0.5];
+%! [X, Y] = meshgrid (linspace (-3, 3, 25));
+%! XY = [X(:), Y(:)];
+%! Z = mvnpdf (XY, mu, sigma);
+%! mesh (X, Y, reshape (Z, size (X)));
+%! colormap jet
+
+%!test
+%! mu = [1,-1];
+%! sigma = [.9 .4; .4 .3];
+%! x = [ 0.5 -1.2; -0.5 -1.4; 0 -1.5];
+%! p = [   0.41680003660313; 0.10278162359708; 0.27187267524566 ];
+%! q = mvnpdf (x, mu, sigma);
+%! assert (p, q, 10*eps);