I have a set of variables (lets say a nx3 : 3 variables and n rows). I
set the mean to be my current data (1x3); data for 3 variables as of
today and set my covariance matrix as the identity matrix. I then
calculate the probability density using the mnvpd function in matlab. In
essence these probability densities are my "distances" from my current
data (my mean variable)
My question is if I want to compute a weighted probability density how do
I do that? if i want to weight one variable 3x the others.
Based on my most recent value(my Mean parameter) the last data point has
the highest weight(closest distance). My question is how do I assign the
4th variable to have for example 3x more weight so that its reflected in
my calculation of densities.
X is my variable matrix, MU = [0.6638 -0.43 -1.56 0.45]
X =
0.7926 -1.1549 -0.9966 0.0520
0.7399 -0.8464 -1.4008 0.1385
0.7428 -0.5986 -1.3788 0.1682
0.3965 -0.4491 -1.2558 0.2441
0.6638 -0.4265 -1.5430 0.4194
Y = mvnpdf(X, MU, eye(4))
Y =
0.0152
0.0218
0.0235
0.0228
0.0253
Y/sum(Y) =
0.1401
0.2004
0.2165
0.2101
0.2329
