True randomness is a system that say generates a 10 digit number and 11111111 has the same odd's as appearing as the other permutations. But then thats not strictly true as we all know any system that monitors the outcome (a good example will be fruit machines) to make sure there is a even distribution of all permutation will in essence remove that level of definition we like to think as truely random.
With that it gets hard to truely say what is random or what is a as yet unknown pattern. This is why many have taken the approach of not having a single source of random numbers but use many and average out from there. There again is that random as the chances with such an approach of getting a high or low value would be biased out.
So with that I postulate one mans random string is another mans non-random string. So with that I define randomness as a yet undertermind sequence or a data. So the included Dilbert post is with that extreemly clever and totaly true.
"hard to truely say what is random or what is a as yet unknown pattern."
No, it's just saying that it's much easier to get pseudorandomness out of a computer than true randomness.
"This is why many have taken the approach of not having a single source of random numbers but use many and average out from there. There again is that random as the chances with such an approach of getting a high or low value would be biased out."
Technically speaking you wouldn't average you'd add them together and take the decimal portion (modulo 1). That can negate bias as long as one of the sources is good even if you don't know which one.
Of course you can remove bias from a single source by Von Neumann's method although this might be computationally harder than the above:
There is actually no such thing as a random or not random number. There are only random and not random generators. Whether a fruit machine is random depends on how is defines/ensures evenness.
Comments
True randomness is a system that say generates a 10 digit number and 11111111 has the same odd's as appearing as the other permutations. But then thats not strictly true as we all know any system that monitors the outcome (a good example will be fruit machines) to make sure there is a even distribution of all permutation will in essence remove that level of definition we like to think as truely random.
With that it gets hard to truely say what is random or what is a as yet unknown pattern. This is why many have taken the approach of not having a single source of random numbers but use many and average out from there. There again is that random as the chances with such an approach of getting a high or low value would be biased out.
So with that I postulate one mans random string is another mans non-random string. So with that I define randomness as a yet undertermind sequence or a data. So the included Dilbert post is with that extreemly clever and totaly true.
"hard to truely say what is random or what is a as yet unknown pattern."
No, it's just saying that it's much easier to get pseudorandomness out of a computer than true randomness.
"This is why many have taken the approach of not having a single source of random numbers but use many and average out from there. There again is that random as the chances with such an approach of getting a high or low value would be biased out."
Technically speaking you wouldn't average you'd add them together and take the decimal portion (modulo 1). That can negate bias as long as one of the sources is good even if you don't know which one.
Of course you can remove bias from a single source by Von Neumann's method although this might be computationally harder than the above:
http://en.wikipedia.org/wiki/Fair_coin#Fair_results_from_a_b...
There is actually no such thing as a random or not random number. There are only random and not random generators. Whether a fruit machine is random depends on how is defines/ensures evenness.