# 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.

Note

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

Distributions.fit_mleMethod
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.
source
Distributions.fit_mleMethod
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.

source

### Applicable distributions

The fit_mle method has been implemented for the following distributions:

Univariate:

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>).