[SI-LIST] Re: Jitter transfer vs. accumulation

Here is a thought experiment that helps me think about "random" jitter, =
which I first encountered when thinking about noise in the ancient days =
when most digital hardware was so slow we didn't have to worry about =
jitter.=20

Could the TIE of a transition ever reach 1000 years? If you argue that =
it could, postulate a condition under which it could do so. (If the PDF =
is truly normal and unbounded, then of course it could.)=20

Or think about some other randomly distributed parameter, such as =
heights of people. As you look at a larger and larger sample of =
individuals, the PDF gets more and more normal looking. But has there =
ever been a 0.0005-inch tall adult human being, or a 700-foot tall =
adult? The normal distribution works fine as a mathematical insight into =
physical processes such as jitter or noise, as long as you don't force =
your thinking out of some normal range of bounds, such as the mass that =
can be supported by a skeleton and musculature, or the height to which =
the heart can pump blood.=20

The central limit theorem reminds us that many phenomena appear Gaussian =
only because they are an accumulation of the effects of a large number =
of non-random processes. I suspect the same is true of most jitter in =
the real world.=20

Art Porter=20
Agilent Technologies     =20

-----Original Message-----
From: si-list-bounce@xxxxxxxxxxxxx [mailto:si-list-bounce@xxxxxxxxxxxxx] =
On Behalf Of Steven Kan
Sent: Monday, March 26, 2007 11:56 AM
To: si-list@xxxxxxxxxxxxx
Subject: [SI-LIST] Re: Jitter transfer vs. accumulation

> From: "Alfred P. Neves" <al.neves@xxxxxxxxxxx>
> Subject: [SI-LIST] Re: Jitter transfer vs. accumulation
> Date: Sat, 24 Mar 2007 09:48:38 -0700
>
> This estimator, peak-peak jitter, is not a good estimator of
> the process since it continues to increase since the process is =
Gaussian
> and collecting more samples digs deeper into the tails of the
> distribution.    The process is by definition unbounded.
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

IANAE, but I've been thinking about this for awhile now. Though our =
finite=20
samples may be a "best fit" with the Gaussian, can we really say that =
any=20
process is truly unbounded when applied to real-world phenomena that =
occur=20
in real-world products? If I apply the constraint that I need to examine =
a=20
given process over the life of the product (or the life of the user or =
the=20
life of the Earth), does that then put bounds the process(es) and the=20
resulting statistics?

I can see the argument from the math side, e.g. "unbounded 'by =
definition'",=20
but do the 'definitions' include practical constraints?

My empirical gold-bar ratio is still zero.=20

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 =20

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