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.
Herman Rubin [EMAIL PROTECTED] wrote in message
9vqoln$[EMAIL PROTECTED]">news:9vqoln$[EMAIL PROTECTED]...
Maximum likelihood is ASYMPTOTICALLY optimal in LARGE
samples. It may not be good for small samples; it pays
to look at how the actual likelihood function behaves.
The fit is always
In article [EMAIL PROTECTED],
Jimc10 [EMAIL PROTECTED] wrote:
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
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