To all,
Thanks so much for all your ideas and insights thus far. To those who have
suggested a Baysean approach, I am interested, but I am weeks away from
understanding it well enough to figure out if I can use it. Also, I think I am
close to developing a usable technique along my current line.
To all who have helped me on the previous thread thank you very much. I am
reposting this beause the question has become more focused.
I am studying a stochastic Markov process and using a maximum likelihood
technique to fit observed data to theoretical models. As a first step I am
using a Monte
I am using a maximum likelihood algorythym to fit an electophysiologic data to
a series of theoretical models. I am interesting in comparing two models wich
differ in the number of free parameters, the simpler being a subhypothesis of
the more complex.
I was told (and have read) that the followi
I am using non-linear regression to fit electophysiological data (current vs t)
to exponential equations. I am using an F-test on the residual sum of squares
to determine how many components are required. A typical trace will have
several thousand points. Question: If I use an adjacent average tec
