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errcw
150 Stars 34 Forks MIT License 38 Commits 0 Opened issues

#### Description

A JavaScript model of the normal distribution

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# gaussian

A JavaScript model of the Normal (or Gaussian) distribution.

## API

### Creating a Distribution

```var gaussian = require('gaussian');
var distribution = gaussian(mean, variance);
// Take a random sample using inverse transform sampling method.
var sample = distribution.ppf(Math.random());
```

### Properties

• `mean`
: the mean (μ) of the distribution
• `variance`
: the variance (σ^2) of the distribution
• `standardDeviation`
: the standard deviation (σ) of the distribution

### Probability Functions

• `pdf(x)`
: the probability density function, which describes the probability of a random variable taking on the value x
• `cdf(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

### Combination Functions

• `mul(d)`
: returns the product distribution of this and the given distribution; equivalent to
`scale(d)`
when d is a constant
• `div(d)`
: returns the quotient distribution of this and the given distribution; equivalent to
`scale(1/d)`
when d is a constant
• `add(d)`
: returns the result of adding this and the given distribution's means and variances
• `sub(d)`
: returns the result of subtracting this and the given distribution's means and variances
• `scale(c)`
: returns the result of scaling this distribution by the given constant

### Generation Function

• `random(n)`
: returns an array of generated
`n`
random samples correspoding to the Gaussian parameters.