Jeffrey
How do we test for randomness, you
described white noise, what are the techniques in Statistics (stochastic or
indeterminate sense) and mathematics?
How does Monte Carlo or Latin Hypercube enter this
determination, i.e. number of trials and distributions for them?
Thanks.
Ravi
(Dr. Ravi Sharma) Senior Enterprise
Architect
Vangent, Inc. Technology Excellence Center (TEC)
8618 Westwood Center Drive,
Suite 310, Vienna VA 22182
(o) 703-827-0638, (c)
313-204-1740 www.vangent.com
From:
ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Schiffel, Jeffrey A
Sent: Tuesday, February 12, 2008
1:08 PM
To: [ontolog-forum]
Subject: Re: [ontolog-forum]
Axiomatic ontology
A series of unfair coin tosses can easily
be random. The distribution of the random variable will not be uniform (as is
the case of a fair coin), but it may still be random. It is just that the
probabilities must be described by a different distribution. Pink noise is a
simple example, since the randomness compared to white noise is red-shifted.
From: Pat Hayes
[mailto:phayes@xxxxxxx]
Sent: Tuesday, February 12, 2008
11:28 AM
To: Barker, Sean (UK)
Cc: [ontolog-forum]
Subject: Re: [ontolog-forum]
Axiomatic ontology
At 10:14 AM +0000 2/12/08, Barker, Sean (UK) wrote:
Pat's claim "The definition of a random sequence is that no matter how
much of it you have, there is no way even in principle to compute any
information about the next item." is true only where you exclude
probabilistic estimates (which you might do depending on how you
interpret "information"). For example, if you encode the tosses of a
coin as a bit stream, as you continue to observe the bit stream, you
will be able to make increasing accurate estimates of the probability
that the next bit will be a 1. Given the additional knowledge that this
is the encoding of coin flips, you will also be able to estimate the
probability that it is a fair coin.
No, wait. A series of tosses of an
unfair coin is not a random sequence. One gets randomness just when
the actual probability of each toss being a head is 0.5 precisely.
What you say above is correct, of course, but it can be translated as:
if a series of bits is not random, this can be detected with increasing
accuracy as the series gets longer. Also, of course, if it is random, this can
also be detected (if it were previously unknown), but that does not mean that
any particular toss can be predicted.
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