A random number generator. Either a fast, insecure mersenne twister or a secure one, depending on which smart constructor is used to construct this type.

Create a random number generator from a Word64 seed. This uses the insecure (but fast) mersenne twister.

Create a new random number generator, using the clocktime as the base for the seed. This must be called from a computation with a lifted base effect of IO.

This is just a conveniently seeded mersenne twister.

Creates a new random number generator, using the system entropy source as a seed. The random number generator returned from this function is cryptographically secure, but not nearly as fast as the one returned by mkRandom and mkRandomIO.

Use a non-random effect as the Random source for running a random effect.

Runs an effectful random computation, returning the computation's result.

Generates a uniformly distributed random number in the inclusive range [a, b].

Generates a uniformly distributed random number in the inclusive range [a, b].

This function is more flexible than uniformIntDist since it relaxes type constraints, but passing in constant bounds such as uniformIntegralDist 0 10 will warn with -Wall.

Generates a uniformly distributed random number in the range [a, b).

NOTE: This code might not be correct, in that the returned value may not be perfectly uniformly distributed. If you know how to make one of these a better way, PLEASE send me a pull request. I just stole this implementation from the C++11 random header.

Generates a linearly-distributed random number in the range [a, b); a with a probability of 0. This code is not guaranteed to be correct.

Produces random boolean values, according to a discrete probability.

k must be in the range [0, 1].

The value obtained is the number of successes in a sequence of t yes/no experiments, each of which succeeds with probability p.

t must be >= 0 p must be in the range [0, 1].

The value represents the number of failures in a series of independent yes/no trials (each succeeds with probability p), before exactly k successes occur.

p must be in the range (0, 1] k must be >= 0

Warning: NOT IMPLEMENTED!

The value represents the number of yes/no trials (each succeeding with probability p) which are necessary to obtain a single success.

geometricDist p is equivalent to negativeBinomialDist 1 p

p must be in the range (0, 1]

Warning: NOT IMPLEMENTED!

The value obtained is the probability of exactly i occurrences of a random event if the expected, mean number of its occurrence under the same conditions (on the same time/space interval) is μ.

Warning: NOT IMPLEMENTED!

The value obtained is the time/distance until the next random event if random events occur at constant rate λ per unit of time/distance. For example, this distribution describes the time between the clicks of a Geiger counter or the distance between point mutations in a DNA strand.

This is the continuous counterpart of geometricDist.

For floating-point α, the value obtained is the sum of α independent exponentially distributed random variables, each of which has a mean of β.

Generates random numbers as sampled from a Weibull distribution. It was originally identified to describe particle size distribution.

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Warning: NOT IMPLEMENTED!

Generates random numbers as sampled from the normal distribution.

Generates a log-normally distributed random number. This is based off of sampling the normal distribution, and then following the instructions at http://en.wikipedia.org/wiki/Log-normal_distribution#Generating_log-normally_distributed_random_variates.

Produces random numbers according to a chi-squared distribution.

Produced random numbers according to a Cauchy (or Lorentz) distribution.

Produces random numbers according to an F-distribution.

m and n are the degrees of freedom.

This distribution is used when estimating the mean of an unknown normally distributed value given n+1 independent measurements, each with additive errors of unknown standard deviation, as in physical measurements. Or, alternatively, when estimating the unknown mean of a normal distribution with unknown standard deviation, given n+1 samples.

Contains a sorted list of cumulative probabilities, so we can do a sample by generating a uniformly distributed random number in the range [0, 1), and binary searching the vector for where to put it.

Performs O(n) work building a table which we can later use sample with discreteDist.

Given a pre-build DiscreteDistributionHelper (use buildDDH), produces random integers on the interval [0, n), where the probability of each individual integer i is defined as w_i/S, that is the weight of the ith integer divided by the sum of all n weights.

i.e. This function produces an integer with probability equal to the weight given in its index into the parameter to buildDDH.

This function produces random floating-point numbers, which are uniformly distributed within each of the several subintervals [b_i, b_(i+1)), each with its own weight w_i. The set of interval boundaries and the set of weights are the parameters of this distribution.

For example, piecewiseConstantDist [ 0, 1, 10, 15 ] (buildDDH [ 1, 0, 1 ]) will produce values between 0 and 1 half the time, and values between 10 and 15 the other half of the time.

This function produces random floating-point numbers, which are distributed with linearly-increasing probability within each of the several subintervals [b_i, b_(i+1)), each with its own weight w_i. The set of interval boundaries and the set of weights are the parameters of this distribution.

For example, `piecewiseLinearDist [ 0, 1, 10, 15 ] (buildDDH [ 1, 0, 1 ])` will produce values between 0 and 1 half the time, and values between 10 and 15 the other half of the time.

Shuffle an immutable vector.

Shuffle a mutable vector.

Yields a set of random from the internal generator, using randomWord64 internally.

Returns a list of bits which have been randomly generated.