# Getting Started

## Installation

The Distributions package is available through the Julia package system by running `Pkg.add("Distributions")`

. Throughout, we assume that you have installed the package.

## Starting With a Normal Distribution

We start by drawing 100 observations from a standard-normal random variable.

The first step is to set up the environment:

```
julia> using Random, Distributions
julia> Random.seed!(123) # Setting the seed
```

Then, we create a standard-normal distribution `d`

and obtain samples using `rand`

:

```
julia> d = Normal()
Normal(μ=0.0, σ=1.0)
julia> x = rand(d, 100)
100-element Array{Float64,1}:
0.376264
-0.405272
...
```

You can easily obtain the `pdf`

, `cdf`

, `quantile`

, and many other functions for a distribution. For instance, the median (50th percentile) and the 95th percentile for the standard-normal distribution are given by:

```
julia> quantile.(Normal(), [0.5, 0.95])
2-element Array{Float64,1}:
0.0
1.64485
```

The normal distribution is parameterized by its mean and standard deviation. To draw random samples from a normal distribution with mean 1 and standard deviation 2, you write:

`julia> rand(Normal(1, 2), 100)`

## Using Other Distributions

The package contains a large number of additional distributions of three main types:

`Univariate`

`Multivariate`

`Matrixvariate`

Each type splits further into `Discrete`

and `Continuous`

.

For instance, you can define the following distributions (among many others):

```
julia> Binomial(p) # Discrete univariate
julia> Cauchy(u, b) # Continuous univariate
julia> Multinomial(n, p) # Discrete multivariate
julia> Wishart(nu, S) # Continuous matrix-variate
```

In addition, you can create truncated distributions from univariate distributions:

`julia> truncated(Normal(mu, sigma), l, u)`

To find out which parameters are appropriate for a given distribution `D`

, you can use `fieldnames(D)`

:

```
julia> fieldnames(Cauchy)
2-element Array{Symbol,1}:
:μ
:β
```

This tells you that a Cauchy distribution is initialized with location `μ`

and scale `β`

.

## Estimate the Parameters

It is often useful to approximate an empirical distribution with a theoretical distribution. As an example, we can use the array `x`

we created above and ask which normal distribution best describes it:

```
julia> fit(Normal, x)
Normal(μ=0.036692077201688635, σ=1.1228280164716382)
```

Since `x`

is a random draw from `Normal`

, it's easy to check that the fitted values are sensible. Indeed, the estimates [0.04, 1.12] are close to the true values of [0.0, 1.0] that we used to generate `x`

.