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.

Maximum Likelihood Estimation

The function fit_mle is for maximum likelihood estimation.


  • 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.
  • 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:


  • bernoulli
  • beta
  • binomial
  • categorical
  • discreteuniform
  • exponential
  • normal
  • gamma
  • geometric
  • laplace
  • pareto
  • poisson
  • uniform


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 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, 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 Conjugate Prior and Posterior).