I am working on a multiple linear regression problem, using simulated annealing optimization. I need to know which is more accurate, the simulated annealing or Bayesian model averaging; their pros and cons, if possible. Thanks in advance.
Only God knows the answer to that for a particular data suggest you look at methods of determining such things. Here's some references: intro to statistical learning by james st al and clinical predictive models by Ewout Steyerberg both by Springer. Good luck with your research, David Booth
Hi! I agree with the previous answers: There's no correct method between the two you propose. I think one way to dicide may be to take a known historical data and analize it using both methods (as Jonathan Davis
said) then you can decide the method that best suits your expected analysis according to the results obtained. Good luck with your analysis!
Thank you for your answers, David Eugene Booth , Jonathan Davis
, Ette Etuk and Ramiro Hernán Riquelme. Actually, the BMA fails while running on my data (using python and determined by Monte Carlo chains) and it takes longer time than the SA algorithm. However, I am going to try another data, may be random, and test it, as you advised. Thank you.- Badges
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