A Typescript model of the Normal (or Gaussian) distribution.
import { Gaussian } from "@searchlight.ai/gaussian-typescript";
const distribution = new Gaussian(mean, variance);
const cdf = distribution.cdf(25);
cdf.add(new Gaussian(1,2))
mean
: the mean (μ) of the distributionvariance
: the variance (σ^2) of the distributionstandardDeviation
: the standard deviation (σ) of the distribution
pdf(x)
: the probability density function, which describes the probability of a random variable taking on the value xcdf(x)
: the cumulative distribution function, which describes the probability of a random variable falling in the interval (−∞, x]ppf(x)
: the percent point function, the inverse of cdf
mul(d)
: updates the product distribution of this and the given distribution;div(d)
: updates the quotient distribution of this and the given distribution;mul_constant(d)
: updatesscale(d)
; equivalent to callingmul(d: number)
div_constant(d)
: updatesscale(1/d)
; equivalent to callingdiv(d: number)
add(d)
: updates the result of adding this and the given distribution's means and variancessub(d)
: updates the result of subtracting this and the given distribution's means and variancesscale(c)
: updates the result of scaling this distribution by the given constant
The original package while great creates a new Gaussian object on every combination function. One slight optimization in this library is that rather than creating a new Gaussian object on every call, we will update our Gaussian's objects instance variables.