Beta
Related Docs:
object Beta
| package distributions
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
Gets one sample from the distribution.
Overridden by filter/map/flatmap for monadic invocations.
Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here
Converts a random sampler of one type to a random sampler of another type.
Converts a random sampler of one type to a random sampler of another type. Examples: randInt(10).flatMap(x => randInt(3 * x.asInstanceOf[Int]) gives a Rand[Int] in the range [0,30] Equivalently, for(x <- randInt(10); y <- randInt(30 *x)) yield y
the transform to apply to the sampled value.
Samples one element and qpplies the provided function to it.
Samples one element and qpplies the provided function to it. Despite the name, the function is applied once. Sample usage:
for(x <- Rand.uniform) { println(x) }
the function to be applied
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
Converts a random sampler of one type to a random sampler of another type.
Converts a random sampler of one type to a random sampler of another type. Examples: uniform.map(_*2) gives a Rand[Double] in the range [0,2] Equivalently, for(x <- uniform) yield 2*x
the transform to apply to the sampled value.
Returns the probability density function at that point.
Returns the probability density function at that point.
Gets n samples from the distribution.
Gets n samples from the distribution.
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
An infinitely long iterator that samples repeatedly from the Rand
An infinitely long iterator that samples repeatedly from the Rand
an iterator that repeatedly samples
Return a vector of samples.
Return a vector of samples.
Returns the probability density function up to a constant at that point.
Returns the probability density function up to a constant at that point.
The Beta distribution, which is the conjugate prior for the Bernoulli distribution