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

Reply via email to