# Distribution Fitting

This package provides methods to fit a distribution to a given set of samples. Generally, one may write

`d = fit(D, x)`

This statement fits a distribution of type `D`

to a given dataset `x`

, where `x`

should be an array comprised of all samples. The fit function will choose a reasonable way to fit the distribution, which, in most cases, is maximum likelihood estimation.

One can use as the first argument simply the distribution name, like `Binomial`

, or a concrete distribution with a type parameter, like `Normal{Float64}`

or `Exponential{Float32}`

. However, in the latter case the type parameter of the distribution will be ignored:

```
julia> fit(Cauchy{Float32}, collect(-4:4))
Cauchy{Float64}(μ=0.0, σ=2.0)
```

## Maximum Likelihood Estimation

The function `fit_mle`

is for maximum likelihood estimation.

### Synopsis

Missing docstring for `fit(D, x)`

. Check Documenter's build log for details.

Missing docstring for `fit(D, x, w)`

. Check Documenter's build log for details.

`Distributions.fit_mle`

— Method`fit_mle(D, x)`

Fit a distribution of type `D`

to a given data set `x`

.

- For univariate distribution, x can be an array of arbitrary size.
- For multivariate distribution, x should be a matrix, where each column is a sample.

`Distributions.fit_mle`

— Method`fit_mle(D, x, w)`

Fit a distribution of type `D`

to a weighted data set `x`

, with weights given by `w`

.

Here, `w`

should be an array with length `n`

, where `n`

is the number of samples contained in `x`

.

### Applicable distributions

The `fit_mle`

method has been implemented for the following distributions:

**Univariate:**

`Bernoulli`

`Beta`

`Binomial`

`Categorical`

`DiscreteUniform`

`Exponential`

`LogNormal`

`Normal`

`Gamma`

`Geometric`

`Laplace`

`Pareto`

`Poisson`

`Rayleigh`

`InverseGaussian`

`Uniform`

`Weibull`

**Multivariate:**

For most of these distributions, the usage is as described above. For a few special distributions that require additional information for estimation, we have to use a modified interface:

```
fit_mle(Binomial, n, x) # n is the number of trials in each experiment
fit_mle(Binomial, n, x, w)
fit_mle(Categorical, k, x) # k is the space size (i.e. the number of distinct values)
fit_mle(Categorical, k, x, w)
fit_mle(Categorical, x) # equivalent to fit_mle(Categorical, max(x), x)
fit_mle(Categorical, x, w)
```

## Sufficient Statistics

For many distributions, the estimation can be based on (sum of) sufficient statistics computed from a dataset. To simplify implementation, for such distributions, we implement `suffstats`

method instead of `fit_mle`

directly:

```
ss = suffstats(D, x) # ss captures the sufficient statistics of x
ss = suffstats(D, x, w) # ss captures the sufficient statistics of a weighted dataset
d = fit_mle(D, ss) # maximum likelihood estimation based on sufficient stats
```

When `fit_mle`

on `D`

is invoked, a fallback `fit_mle`

method will first call `suffstats`

to compute the sufficient statistics, and then a `fit_mle`

method on sufficient statistics to get the result. For some distributions, this way is not the most efficient, and we specialize the `fit_mle`

method to implement more efficient estimation algorithms.

## Maximum-a-Posteriori Estimation

Maximum-a-Posteriori (MAP) estimation is also supported by this package, which is implemented as part of the conjugate exponential family framework (see :ref:`Conjugate Prior and Posterior <ref-conj>`

).