That's right. I'm stress-testing my various methods by seeing how soon they detect a step.
A perennial cry from the product managers was "how many more subjects do we need to test before we can be confident performance has actually got better?" (ie there's been a step-up in expected performance). Ian On Wed, Jul 4, 2012 at 8:32 AM, Bo Jacoby <bojac...@yahoo.dk> wrote: > That explains it! With N=100, A=90, B=20, C=89 you have 90 samples to the > left to estimate A~89 but only 10 samples to the right to estimate B~37. This > is reasonable. > > - Bo > > Til: Programming forum <programming@jsoftware.com> >>Sendt: 23:05 tirsdag den 3. juli 2012 >>Emne: Re: [Jprogramming] Kalman filter in J? >> >>@Bo The discrepancy comes about because I altered your original verb: >>C for compatibility with my own definition of the Heaviside function: >>hv ... >> >> 10 hv 3 >>0 0 0 1 1 1 1 1 1 1 >> >>I did this simply by increasing its returned value by 1. But I did not >>alter the old version of ABC (ABC0) which uses it. (It does need >>altering). >> >>Upon restoring to your original C, I can confirm that ABC0 now returns >>the exact correct parameters in the absence of noise (it didn't >>before) ... >> >> ABC0 10 hv 3 >>0 1 2 >> >>and that both versions now give identical results on the few trials >>I've made of them, especially with the random variable X which I was >>using for the test... >> >> ABC0 X >>98.6542 36.7969 89 >> ABC X >>98.6542 36.7969 89 >> >>This version of X (which I've saved and will make available) is a hard >>test: when I plot it I can't see the step without knowing where it's >>meant to be. The standard deviation of the Gaussian noise is half the >>step height, 10. Thus the noise is capable of obliterating the >>information as to the exact onset of the step, so I'm impressed that >>ABC gets it right here, even if the estimate of B is awry in this >>instance. (In the absence of noise it would be 100 20 89.) >> >>With other X's I've tried, ABC makes far better estimates of B. >> >>Ian >> >>On Tue, Jul 3, 2012 at 5:17 PM, Bo Jacoby <bojac...@yahoo.dk> wrote: >>> The old ABC program and the new ABC program were supposed to be equivalent >>> algorithms, and so they should give exactly the same results for the same >>> test data. That is why I am bewildered by the difference. Ian did the test >>> and knows about the test data. >>> - Bo >>>>________________________________ >>>> Fra: Raul Miller <rauldmil...@gmail.com> >>>>Til: Programming forum <programming@jsoftware.com> >>>>Sendt: 16:34 tirsdag den 3. juli 2012 >>>>Emne: Re: [Jprogramming] Kalman filter in J? >>>> >>>>On Tue, Jul 3, 2012 at 6:08 AM, Bo Jacoby <bojac...@yahoo.dk> wrote: >>>>> The result changes from C=90 (correct) in the old ABC to C=89 >>>>> (incorrect) in the new ABC. Also the old ABC result B=41 and the >>>>> new ABC result B=37 should both be B=20. That is bewildering. >>>> >>>>Which test data are you using here (for X)? >>>> >>>>(Have you verified that the noise does not shift the values of the >>>>"best result"?) >>>> >>>>-- >>>>Raul >>>>---------------------------------------------------------------------- >>>>For information about J forums see http://www.jsoftware.com/forums.htm >>>> >>>> >>>> >>> ---------------------------------------------------------------------- >>> For information about J forums see http://www.jsoftware.com/forums.htm >>---------------------------------------------------------------------- >>For information about J forums see http://www.jsoftware.com/forums.htm >> >> >>Fra: Ian Clark <earthspo...@gmail.com> > ---------------------------------------------------------------------- > For information about J forums see http://www.jsoftware.com/forums.htm ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
