I know this goes against the definition of random numbers, but still I require this for my project. For instance, I want to generate an array with 5 random elements in range(0, 200).

## C++ random yields different numbers for same Mersenne Twister seed when using float precision

I need to run reproducible Monte Carlo runs. That means I use a known seed that I store with my results, and use that seed if I need to run the same problem instance using the same random numbers. This is common practice.

## Generate uniformly random float which can return all possible values

An easy way to generate a random float64 in [0,1) is by generating a uniformly random int in [0,2⁵³) and dividing it by 2⁵³. This is essentially what rand.Float64() is doing. However, not all possible float64 values between 0 and 1 can be generated this way: if the value is...

## Why is random number generator tf.random_uniform in tensorflow much faster than the numpy equivalent

The following code is what I used to test the performance:All the three segments generate a uniformly random 1000*2000 matrix in double precision 400 times. The timing differences are striking. On my Mac,

## Why is the new random library better than std::rand()?

So I saw a talk called rand() Considered Harmful and it advocated for using the engine-distribution paradigm of random number generation over the simple std::rand() plus modulus paradigm.

## Is the new random library really better than std::rand()?

## Why is the use of rand() considered bad?

I heard some guys telling that the use of rand() is bad EVEN AFTER USING srand() to get a seed. Why is that so? I want to know how the stuff happens... And sorry for another question.. but what is an alternative to this then?

## Why do I get this particular color pattern when using rand()?

I tried to create a bmp file, like this:I excepted to get something random (white noise). However, the output is interesting:

## Best approach to random 10 numbers between 1 and 100 no dupes in javascript? [duplicate]

This question already has an answer here:This has been asked dozens of times, but somehow, after reading many answers, I'm not convinced. I'm not cleared about the best way to do it, performance and code simplicity.

## Is java.util.Random really that random? How can I generate 52! (factorial) possible sequences?

I've been using Random (java.util.Random) to shuffle a deck of 52 cards. There are 52! (8.0658175e+67) possibilities. Yet, I've found out that the seed for java.util.Random is a long, which is much smaller at 2^64 (1.8446744e+19).