LA home
 N χ
 Eigen v.
 Least squares
 Lagrange mult.

Also see
Stein's Lemma:
E[ g(x) * (x-μ) ] = E[ g'(x) ]
where g(x) is an everywhere differentiable function of x, and μ is a location parameter (--Daniel).
If h is a "smooth" function of x, and E[X]=μ & variance[X]=ν, the expectation
E[ h(x) ]
= E[ h(μ) + (x-μ) . h'(μ) + (x-μ)2/2 . h''(μ) + ... ]   --by Taylor expansion about μ
= h(μ) + (ν/2)h''(μ) + ...
which may come in useful for approximating Fisher information, etc. (--DS).
www #ad:

↑ © L. Allison,   (or as otherwise indicated).
Created with "vi (Linux)",  charset=iso-8859-1,   fetched Monday, 22-Jul-2024 23:27:31 UTC.

Free: Linux, Ubuntu operating-sys, OpenOffice office-suite, The GIMP ~photoshop, Firefox web-browser, FlashBlock flash on/off.