Quote:
Originally Posted by IllSkill
Is there like a link or something to whoever verified their RNG as legitimate. It's just too much for me, i really truly in my heart of hearts believe there is something going on with the RNG. It's truly unbelievable. And i don't say this stuff lightly. Especially that huds aren't allowed. There is no way for players to verify this.
I never been a big fan of their rng they use Mersenne Twister. Its very old out dated rng from 1997. Not rigged or anything (variance is sick in poker) but doesn't hurt to upgrade to a more secure one that works faster. Here is a list of disadvantages and better alternatives if they do ever want to update it.
Disadvantages
Relatively large state buffer, of 2.5 kib, unless the TinyMT variant (discussed below) is used.
Mediocre throughput by modern standards,[39] unless the SFMT variant (discussed below) is used.
Exhibits two clear failures (linear complexity) in both Crush and BigCrush in the TestU01 suite. There are a number of other generators that do pass all the tests (and numerous generators that fail badly).
Multiple instances that differ only in seed value (but not other parameters) are not generally appropriate for Monte-Carlo simulations that require independent random number generators, though there exists a method for choosing multiple sets of parameter values.
Can take a long time to start generating output that passes random tests if the initial state is highly non-random—particularly if the initial state has many zeros. A consequence of this is that two instances of the generator, started with initial states that are almost the same, will usually output nearly the same sequence for many iterations, before eventually diverging. The 2002 update to the MT algorithm has improved initialization, so that beginning with such a state is very unlikely.
Is not cryptographically secure , unless the CryptMT variant (discussed below) is used. The reason is that observing a sufficient number of iterations (624 in the case of MT19937, since this is the size of the state vector from which future iterations are produced) allows one to predict all future iterations.
An alternative generator, WELL ("Well Equidistributed Long-period Linear"), offers quicker recovery, and equal randomness, and nearly equal speed.
Marsaglia's xorshift generators and variants are the fastest in this class.
64-bit MELGs ("64-bit Maximally Equidistributed F2-Linear Generators with Mersenne Prime Period") are completely optimized in terms of the k-distribution properties.
The ACORN family (published 1989) is another k-distributed PRNG, which shows similar computational speed to MT, and better statistical properties as it satisfies all the current (2019) TestU01 criteria; when used with appropriate choices of parameters, ACORN can have arbitrarily long period and precision.