case class Gaussian(mu: Double, sigma: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable
Represents a Gaussian distribution over a single real variable.
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- Gaussian
- Product
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- HasInverseCdf
- HasCdf
- Moments
- ContinuousDistr
- Rand
- Serializable
- Serializable
- Density
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
##(): Int
- Definition Classes
- AnyRef → Any
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final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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def
apply(x: Double): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
- Definition Classes
- ContinuousDistr → Density
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
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def
cdf(x: Double): Double
Computes the cumulative density function of the value x.
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def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
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- Annotations
- @native() @HotSpotIntrinsicCandidate() @throws( ... )
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def
condition(p: (Double) ⇒ Boolean): Rand[Double]
- Definition Classes
- Rand
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def
draw(): Double
Gets one sample from the distribution.
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def
drawOpt(): Option[Double]
Overridden by filter/map/flatmap for monadic invocations.
Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here
- Definition Classes
- Rand
- def entropy: Double
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
filter(p: (Double) ⇒ Boolean): Rand[Double]
- Definition Classes
- Rand
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def
flatMap[E](f: (Double) ⇒ Rand[E]): Rand[E]
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
- f
the transform to apply to the sampled value.
- Definition Classes
- Rand
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def
foreach(f: (Double) ⇒ Unit): Unit
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) }- f
the function to be applied
- Definition Classes
- Rand
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def
get(): Double
- Definition Classes
- Rand
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
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def
inverseCdf(p: Double): Double
Computes the inverse cdf of the p-value for this gaussian.
Computes the inverse cdf of the p-value for this gaussian.
- returns
x s.t. cdf(x) = numYes
- Definition Classes
- Gaussian → HasInverseCdf
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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def
logApply(x: Double): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
- Definition Classes
- ContinuousDistr → Density
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lazy val
logNormalizer: Double
- Definition Classes
- Gaussian → ContinuousDistr
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def
logPdf(x: Double): Double
- Definition Classes
- ContinuousDistr
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def
map[E](f: (Double) ⇒ E): Rand[E]
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
- f
the transform to apply to the sampled value.
- Definition Classes
- Rand
- def mean: Double
- def mode: Double
- val mu: Double
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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lazy val
normalizer: Double
- Definition Classes
- Gaussian → ContinuousDistr
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final
def
notify(): Unit
- Definition Classes
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- Annotations
- @native() @HotSpotIntrinsicCandidate()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
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def
pdf(x: Double): Double
Returns the probability density function at that point.
Returns the probability density function at that point.
- Definition Classes
- ContinuousDistr
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def
probability(x: Double, y: Double): Double
Computes the probability that a Gaussian variable Z is within the interval [x, y].
Computes the probability that a Gaussian variable Z is within the interval [x, y]. This probability is computed as P[Z < y] - P[Z < x].
- x
lower-end of the interval
- y
upper-end of the interval
- returns
probability that the Gaussian random variable Z lies in the interval [x, y]
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def
sample(n: Int): IndexedSeq[Double]
Gets n samples from the distribution.
Gets n samples from the distribution.
- Definition Classes
- Rand
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def
sample(): Double
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
- Definition Classes
- Rand
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def
samples: Iterator[Double]
An infinitely long iterator that samples repeatedly from the Rand
An infinitely long iterator that samples repeatedly from the Rand
- returns
an iterator that repeatedly samples
- Definition Classes
- Rand
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def
samplesVector[U >: Double](size: Int)(implicit m: ClassTag[U]): DenseVector[U]
Return a vector of samples.
Return a vector of samples.
- Definition Classes
- Rand
- val sigma: Double
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
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def
toString(): String
- Definition Classes
- Gaussian → AnyRef → Any
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def
unnormalizedLogPdf(t: Double): Double
- Definition Classes
- Gaussian → ContinuousDistr
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def
unnormalizedPdf(x: Double): Double
Returns the probability density function up to a constant at that point.
Returns the probability density function up to a constant at that point.
- Definition Classes
- ContinuousDistr
- def variance: Double
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final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- Annotations
- @throws( ... )
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final
def
wait(arg0: Long): Unit
- Definition Classes
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final
def
wait(): Unit
- Definition Classes
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- @throws( ... )
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def
withFilter(p: (Double) ⇒ Boolean): Rand[Double]
- Definition Classes
- Rand