Re:Random(Random(Random(Random())))
by godelski on Tuesday May 16, 2023 @01:41AM
(#35958083)
> not equal to random()^3
Let's clarify what this means, that's probably the confusion. Here's python code
from random import random
x = random()
z = x*3
That's the random()^3 term. But let's try to differentiate this
a,b,c = random(), random(), random()
z = a * b * c
You may notice that the z's don't match in these cases. The key difference is that in the exponent version we draw a single random variable (remember it is >0 and <1). Then we take this value and multiply it by itself (2 times). This corresponds to rolling a dice once and then multiplying the value by itself 3 times (x*x*x). But in the other case we have 3 independent random variables. This is like rolling the dice 3 times and multiplying the 3 different values. In the second case we have more ways, but more possibilities (What I suggest doing is plotting a histogram of this data (10k samples with 1k bins will be sufficient). Make 3 plots (preferably using subplot). I went ahead and did this[0].
Let's actually simplify this a bit[1] and work it out by hand: we'll look at r^2 vs r*r (where r is an independent random uniform variable). We'll look at an upper bound and the middle.
So in r^2 90% of our r's will be under 0.9, with 9% of values in r^2 being <0.81. Now the middle case, r=0.5. Half our values are above 0.25, and half are <0.25. From this we can see 3 bins: <0.25/0.25 - 0.81/>0.81. 50% of our data is <0.25, 40% is between 0.25 and 0.81, and 10% is >0.81. We can tell from these three bins that our data is pushed towards 0 and we have a long tail.
Now let's look at the r*r case. We'll need to write out more possibilities because we have more cases. 0.9*0.9=0.81, 0.5*0.9=0.9*0.5=0.45, 0.5*0.5=0.25. You see we've added an extra bin because the r's don't have to be the same (lucky for symmetry though). Our bins are <0.25/0.25-0.45/0.45-0.81/>0.81. This now corresponds to 25% of the data, 20% of the data, 36% of the data, and 19%. If we look at these bins, we can see that this is flatter. In the r^2 case we had 50% of our data under 0.25 but in the r*r case we only have 25%! Similarly in the r^2 case we had 10% of our data >0.81 compared to 19%! So we should expect a similar shape but flatter.
I hope this helps.
Quiz to test your understanding:
We're going to make a bet and I'm giving you two options. In the first option I'll allow you to roll a dice, take the value, and cube it. In the second case I'll allow you to roll 3 dice and multiply all values. You win if your result is >63. Which option do you choose? What if you win when the result is <8? (I'm sure someone will post an answer)
Let's clarify what this means, that's probably the confusion. Here's python code
from random import random
x = random()
z = x*3
That's the random()^3 term. But let's try to differentiate this
a,b,c = random(), random(), random()
z = a * b * c
You may notice that the z's don't match in these cases. The key difference is that in the exponent version we draw a single random variable (remember it is >0 and <1). Then we take this value and multiply it by itself (2 times). This corresponds to rolling a dice once and then multiplying the value by itself 3 times (x*x*x). But in the other case we have 3 independent random variables. This is like rolling the dice 3 times and multiplying the 3 different values. In the second case we have more ways, but more possibilities (What I suggest doing is plotting a histogram of this data (10k samples with 1k bins will be sufficient). Make 3 plots (preferably using subplot). I went ahead and did this[0].
Let's actually simplify this a bit[1] and work it out by hand: we'll look at r^2 vs r*r (where r is an independent random uniform variable). We'll look at an upper bound and the middle.
So in r^2 90% of our r's will be under 0.9, with 9% of values in r^2 being <0.81. Now the middle case, r=0.5. Half our values are above 0.25, and half are <0.25. From this we can see 3 bins: <0.25/0.25 - 0.81/>0.81. 50% of our data is <0.25, 40% is between 0.25 and 0.81, and 10% is >0.81. We can tell from these three bins that our data is pushed towards 0 and we have a long tail.
Now let's look at the r*r case. We'll need to write out more possibilities because we have more cases. 0.9*0.9=0.81, 0.5*0.9=0.9*0.5=0.45, 0.5*0.5=0.25. You see we've added an extra bin because the r's don't have to be the same (lucky for symmetry though). Our bins are <0.25/0.25-0.45/0.45-0.81/>0.81. This now corresponds to 25% of the data, 20% of the data, 36% of the data, and 19%. If we look at these bins, we can see that this is flatter. In the r^2 case we had 50% of our data under 0.25 but in the r*r case we only have 25%! Similarly in the r^2 case we had 10% of our data >0.81 compared to 19%! So we should expect a similar shape but flatter.
I hope this helps.
Quiz to test your understanding:
We're going to make a bet and I'm giving you two options. In the first option I'll allow you to roll a dice, take the value, and cube it. In the second case I'll allow you to roll 3 dice and multiply all values. You win if your result is >63. Which option do you choose? What if you win when the result is <8? (I'm sure someone will post an answer)
[0] https://i.imgur.com/gQ5uqED.png
[1] I started writing out the full case but it got long winded so I deleted. I can redo if needed.