Understanding random numbers

woodturner550

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I'm not a student, but have a math question. I want the best random numbers I can get. I'm looking at what defines a random number set. I think that "Best Possible Mean minus Standard Deviation" may be the way to get great random numbers. Any thoughts would be helpful. There is no definitive standard for random numbers according to NIST.

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I'm not a student, but have a math question. I want the best random numbers I can get. I'm looking at what defines a random number set. I think that "Best Possible Mean minus Standard Deviation" may be the way to get great random numbers. Any thoughts would be helpful. There is no definitive standard for random numbers according to NIST.

View attachment 36827
It isn't quite clear what you are asking for. You say you want a way to get [pseudo]random numbers; but it looks to me like you are looking for a good way to measure how random a random number generator is.

And your work looks like maybe you are making measurements of four sets of 10000 random 1s and 0s generated by Excel. I have no idea what you are calculating, or why.

Please clarify. Maybe a reference to NIST, and whatever source you used for your "best possible mean - standard deviation" idea, would show us more about what you are thinking.
 
I'm looking at what makes a random number random, as well as how to qualify random numbers. If I think about the "Best Possible Mean", that would be what you want your random numbers to closely relate to. I need a base line to work from. Therfore I think the distance between "Best Possible Mean" and Standard Deviation is very important. This is what I would like your thoughts on. I'm chasing the mathematical definition of "Random".
I'm looking for the best base to work from. I challenge everything. I believe that digital computers CAN make "real" random numbers. I know digital computers are deterministic as understood today. If you add an unknowable properly the deterministic system becomes indeterminate.
When you use a stop watch to time an event for example. You push the button to start the 'event', you cannot know the exact nanosecond that the event started. All you know is that you marked the beginning of the event. Even on a computer you can't know the exact nanosecond an event started. If this is true, then good so far.
This 'time' at the start of an event is what we want for the 'seed' for random numbers. It's how 'time' is used to make the seed that is important. Using 'time' alone does not work at all. I've found out how to use 'time' Nanosecond time to make perfect 'seeds'. That's where the data that I showed is from.
Any thoughts are welcome.
I tried to up load my data file but it is to big for the forum. It's just binary in columns. I could just send part of it. I'll do that.
 
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I tried to get Excel 365 to PDF file. File is not correct as it drops small numbers into #######. Sorry. Still to big file.
 
I'm looking at what makes a random number random, as well as how to qualify random numbers.
You said that you are no mathematician, but your question goes right into the middle of statistic tests and the definition of probability distributions and random variables. There is no easy answer to whether specific numbers are pseudo-random or not.

We don't even know for sure whether the digits of certain numbers like [imath] \pi, e,\sqrt{2},\log 2 [/imath] to the base [imath] 10 [/imath] are pseudo-random or not. The fact that we assume they are means that they passed several known tests. Already the Wikipedia link I posted above uses a mathematical language only to describe the problem. In that case, we use the relative frequency of [imath] \{0,1,2,3,4,5,6,7,8,9\} [/imath] in increasingly longer sections of digits as a measure. If you only have the digits [imath] \{0,1\} [/imath], then they should occur equally often in very, very, very long sections of outputs, i.e. no bias in the long run. The digits should be uniformly distributed.
 
I agree "The digits should be uniformly distributed.". That to has to be looked at. If following down a list of random numbers they will cross the "Best Possible Mean" many times in a long list of random binary. There is a sign wave to this. The sign wave is those above or below the "Best Possible Mean". I don't have a chart handy, however, you will see the sign wave crosses the Mean once at about 317
1702684163513.png

That is why I'm asking about using "Best Possible Mean" Minus Standard Deviation as a mathematical base. Without a logical base no advancement on "random" numbers can happen.
 
I agree "The digits should be uniformly distributed.". That to has to be looked at. If following down a list of random numbers they will cross the "Best Possible Mean" many times in a long list of random binary. There is a sign wave to this. The sign wave is those above or below the "Best Possible Mean". I don't have a chart handy, however, you will see the sign wave crosses the Mean once at about 317
View attachment 36835

That is why I'm asking about using "Best Possible Mean" Minus Standard Deviation as a mathematical base. Without a logical base no advancement on "random" numbers can happen.
First, please define "sign wave" as you are using it. If you mean "sine wave", there is no such thing here -- just, perhaps, an oscillation (more like a random walk than a periodic function). It's important to say clearly what you mean, and to use proper terminology if you hope to communicate with others.

Most important, please explain what you mean by "Best Possible Mean Minus Standard Deviation". What is it, specifically, and where did this idea originate? Then, explain what you are saying about "crossing the mean": What is your claim, and what do you base it on?

There is no definitive standard for random numbers according to NIST.
My impression is that you have seen something like this (from NIST),


and think you (or we?) can define "random" better than these experts can.
 
Thanks for your input! I miss wrote (sign wave vs. sinewave), sorry.

First, I don’t think I know better than everyone else! I’m challenging myself mentally on something that has not been settled mathematically. Maybe I will see something different.

Define “Best Possible Mean: Is the same as Mean, add all numbers in list and divide by number of items. I write it this way so it is understood that the best it can be is “Best Possible Mean”. Working with binary the “Best Possible Mean” is: 0 + 1 = (1 / 2) = 0.5.

Define Standard Deviation: A standard deviation (or σ) is a measure of how dispersed the data is in relation to the mean. Low, or small, standard deviation indicates data are clustered tightly around the mean, and high, or large, standard deviation indicates data are more spread out.

The closer to “Best Possible Mean” and random is what the goal is. Therefore “Best Possible Mean” minus Standard Deviation is a concrete base for random, I believe. It gives a definitive way of working with random numbers. Without a base no further work can be done. What are you measuring each “random number generator” against the others ”random number generator.

Random binary will have a sinewave. This chart is not the best lay out, but it shows that there is a sinewave. The “Best Possible Mean” is in the middle. Sinewaves come in many different forms. Not always nice and smooth. The binary in the chart is as follows: 1011100011000

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As far as NIST information. I contacted NIST to get the “Standard for Random Number “, was informed there is no such thing! Only a set of math functions that are not definitive. I have them. Anyone can get them from NIST.

All constructive thoughts are needed. Thanks. Just to state this, I have been working on this problem for many years.
 
As far as I know this is the best definition of "Random".

1. It looks random. This means that it passes all the statistical tests of randomness that we can find.

2. It is unpredictable. It must be computationally infeasible to predict what the next random bit will be, given complete knowledge of the algorithm or hardware generating the sequence and all of the previous bits in the stream.

3. It cannot be reliably reproduced. If you run the sequence generator twice with the exact same input (at least as exact as humanly possible), you will get two completely unrelated random sequences.



The output of a generator satisfying these three properties will be good enough for a one-time pad, key generation, and any other cryptographic applications that require a truly random sequence generator.
 
The closer to “Best Possible Mean” and random is what the goal is. Therefore “Best Possible Mean” minus Standard Deviation is a concrete base for random, I believe. It gives a definitive way of working with random numbers.
Please explain why you believe this. Where did this idea come from? Mathematics is not a matter of something feeling right; if you don't have the background to prove a claim, then you shouldn't make it.

But you still haven't said that you claim to be calculating. Are you saying you are calculating the mean of all the random digits you get over, say, 10,000 repetitions, and subtracting from that the standard deviation of those digits? What do you do with that? Do you want to use that in some way to judge the randomness of your data? How?

It's true that you would expect the mean of the data to be about 0.5 over the long run; perhaps that's what you mean by "best possible". There's also an expected value for the standard deviation of the data in a uniform distribution. But I don't know why you would subtract that from the mean.

Have you studied enough probability theory to know how much variation to expect from that, if the data are random?

Without a base no further work can be done. What are you measuring each “random number generator” against the others ”random number generator.
This is what NIST is trying to do with the paper I linked to (for one particular application of random numbers, namely cryptography)). If you're saying no one should be making random numbers because there is no one standard, then you are being silly. And to try to propose a "standard" with no proof that it makes sense, and no clear description of exactly what you mean is even sillier.

Random binary will have a sinewave. This chart is not the best lay out, but it shows that there is a sinewave. The “Best Possible Mean” is in the middle. Sinewaves come in many different forms. Not always nice and smooth.
You don't know what a sine wave is. It is strictly periodic, and has a very specific form. I think what you are talking about is a periodic function of some sort; but random anything will by definition not be periodic. It will vary randomly.

But, again, on what grounds do you claim that it will "have a sine wave"?

As far as NIST information. I contacted NIST to get the “Standard for Random Number “, was informed there is no such thing! Only a set of math functions that are not definitive. I have them. Anyone can get them from NIST.
So you think something more can be said than they have said about defining what random means. Whether you think you or we can do that, why would you think it's a sensible thing to try to do?

The output of a generator satisfying these three properties will be good enough for a one-time pad, key generation, and any other cryptographic applications that require a truly random sequence generator.
This is what NIST is trying to make more precise. Have you read the paper, or other materials from them?
 
The data is from my twenty-two line “real random number generator” program in Python 3.7. I am happy to share the code of the random number generator. Knowledge is for all freely in my opinion.

This is the Standard Deviation of 400 sets of 10,000 random binary bits (0,1) broken into four equal parts. Each part comprises of 100 sets of 10,000 random bit, 1,000,000 bits total. The four groups together is 4,000,000 bits. The best that can be achieved is 0.5., and that is not random, it is in order.

0.499993153 - 0.5 = -0.000007

0.500000244 – 0.5 = 0.00000002.44

0.499995232 – 0.5 = -0.000005

0.4999945 – 0.5 = -0.000006



I always give data from a data set so it can be verified if needed.
 
I am a seventy-three-year-old, 60% disabled veteran, self-educated. In 1995 I owned and engineered an Internet Serve Provider. I say this to impart that I’ve been working with computers most of my life. The problem of “real random numbers” has been thirty-five years of work and fun.

This knowledge will affect: Number Theory; Linear and Multilinear Algebra; Potential Theory; Statistics; Numerical Analysis; Statistical Mechanics, Structure of Matter.

As soon as it is recognized as correct, then I can explain what and how this program came into being. There is a lot of great research to be done now. I just opened the door to a place to investigate. Random numbers are very interesting. This advancement will make digital computers much more powerful.

I am hoping my work will inspire others to explore and challenge everything. Must follow the laws of nature and logic. A word about ‘logic’. It is the one thing that I use that is not taught in schools. It is as important as understanding the laws of nature or physics.

Any thoughts are welcome. The facts will stand up to the ‘hard look’ by anyone. There is more that I have learned but cannot be understood till ‘real random numbers’ CAN be made by digital computers is understood as fact.

Dr.Peterson your thoughts please.


Dr.Peterson

 
This is being looked at in the University of Oregon. It is the process of checking if everything is as I say it is. New knowledge.
Thanks fore your help, everyone.
 
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