Your mean square error procedure is slightly incorrect. You should take the
final signals from both processes, say A[1..n] and B[1..n], subtract them to
get your error signal E[1..n], then the mean square error is the sum of the
squared error over n.
Sum( E[1..n]^2 ) / n
This (MSE) is a
On 3/7/13 10:10 AM, volker böhm wrote:
dear all,
i'm trying to meassure the difference between two equivalent but not identical
processes.
i sorta know know what you mean by this, maybe... but it would be
interesting to see an articulated definition of what makes processes
equivalent
On 07.03.2013, at 16:27, Thomas Young wrote:
Your mean square error procedure is slightly incorrect. You should take the
final signals from both processes, say A[1..n] and B[1..n], subtract them to
get your error signal E[1..n], then the mean square error is the sum of the
squared error
On 07.03.2013, at 16:32, robert bristow-johnson wrote:
now i'm looking for something to quantify the error signal.
from statistics i know there is something like the mean squared error.
so i'm squaring the error signal and take the (running) average.
mostly i'm getting some numbers very
On 3/7/13 1:41 PM, volker böhm wrote:
On 07.03.2013, at 16:32, robert bristow-johnson wrote:
now i'm looking for something to quantify the error signal.
from statistics i know there is something like the mean squared error.
so i'm squaring the error signal and take the (running) average.
