[SciPy-user] optimization advice needed

David Cournapeau david@ar.media.kyoto-u.ac...
Mon Jan 28 21:10:18 CST 2008

Andrew Straw wrote:
> If f() is stationary and you are trying to estimate a and b, isn't this 
> exactly the case of a Kalman filter for linear f()? And if f() is 
> non-linear, there are extensions to the Kalman framework to handle this.
Even Kalman is overkill, I think, no (if f is linear) ? A simple wiener 
filter may be enough, then.

Neal, I think the solution will depend on your background and how much 
time you want to spend on it (as well as the exact nature of the problem 
you are solving, obviously, such as can you first estimate the model on 
some data offline, and after estimate new data online, etc...): if you 
have only a couple of hours to spend, and you don't have background in 
bayesian statistics, I think it will be overkill.

A good introduction in the spirit of what Gael suggested (as I 
understand it) is to read the first chapter and third chapter of the 
book "pattern recognition and machine learning" by C. Bishop. That's the 
best, almost self-contained introduction I can think of on the top of my 



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