Re: Maximum Likelihood Estimation
- To: mathgroup at christensen.cybernetics.net
- Subject: [mg1135] Re: Maximum Likelihood Estimation
- From: pehowland at taz.dra.hmg.gb (Paul E. Howland)
- Date: Wed, 17 May 1995 03:19:17 -0400
- Organization: Defence Research Agency
In article <3ocfkr$o3o at news0.cybernetics.net>, Bill Harbaugh <harbaugh at students.wisc.edu> writes: >One approach is to write out the likelihood function and then do a grid >search, assuming it is too nasty for analytic methods. I am doing this >at the moment, and can tell you that mma is very, very, very slow for >this purpose. My bet is that you would be much happier with a package >designed for this purpose, such as Gauss. On the otherhand, the symbolic >abilities of mma can be useful for some problems Rather than a grid search, why not try a "natural algorithm", such as minimisation by simulated annealing or a genetic algorithm. These are basically empirical methods that randomly sample the error surface using an algorithm analogous to something in nature (simulated annealing === cooling of a crystaline substance, genetic algorithm === Darwin's ideas). These will tend to locate the global minimum of an error surface, unlike more traditional search methods such as Steepest Descent, Gauss-Newton or Levenberg-Marquardt, which tend to fall down the nearest local minimum. You can use the natural algorithm to approximately locate the global minimum, and then use a traditional method (as implemented in NonlinearFit and FindMinimum) to rapidly converge on the least squares answer. I've written my own genetic algorithm (GA) in Mathematica for this purpose, and it is very effective - surprisingly so. Since writing my GA, one has appeared on MathSource - so it is now available to all (I haven't tried it though). Only use a grid search as a last resort - especially if you have a large number of parameters. Paul E Howland Long Range Ground Radar Systems Section tel. +44 (0)1684 895767 CSS2 Division, Room BY209 fax. +44 (0)1684 896315 Defence Research Agency email: PEHOWLAND at DRA.HMG.GB Malvern, Worcs, WR14 3PS, UK. -----------------------------------------------------------------------------