NMinimize vs NonlinearModelFit
- To: mathgroup at smc.vnet.net
- Subject: [mg114450] NMinimize vs NonlinearModelFit
- From: Alex <axel.kowald at rub.de>
- Date: Sat, 4 Dec 2010 06:17:08 -0500 (EST)
I would like to fit some model parameter to numerical data and so
NonlinearModelFit seems to be the right choice. It also gives nice
confidence intervals and other statistics for the obtained parameter
values, but then NMinimize can use the DifferentialEvolution
algorithm, which is not available to NonlinearModelFit. I would also
like to put constraints on my parameters (lower & upper limit), but it
seems that both methods can do this.
Basically I wonder what method other people are using for fitting more
complex models (10-15 parameters) to numerical data ?
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