negloglik

Negative loglikelihood of probability distribution

Syntax

nll = negloglik(pd)

Description

nll = negloglik(pd) returns the value of the negative loglikelihood function for the data used to fit the probability distribution pd.

Examples

Negative Log Likelihood for a Fitted Distribution

Load the sample data.

load carsmall

Create a Weibull distribution object by fitting it to the mile per gallon (MPG) data.

pd = fitdist(MPG,'Weibull')

pd = 
  WeibullDistribution

  Weibull distribution
    A = 26.5079   [24.8333, 28.2954]
    B = 3.27193   [2.79441, 3.83104]

Compute the negative log likelihood for the fitted Weibull distribution.

wnll = negloglik(pd)

wnll = 327.4942

Negative Loglikelihood for a Kernel Distribution

Load the sample data. Fit a kernel distribution to the miles per gallon (MPG) data.

load carsmall;
pd = fitdist(MPG,'Kernel')

pd = 
  KernelDistribution

    Kernel = normal
    Bandwidth = 4.11428
    Support = unbounded

Compute the negative loglikelihood.

nll = negloglik(pd)

nll = 327.3139

Input Arguments

pd — Probability distribution

probability distribution object

Probability distribution, specified as one of the following probability distribution objects created by fitdist or Distribution Fitter:

• BetaDistribution

• BinomialDistribution

• BirnbaumSaundersDistribution

• BurrDistribution

• ExponentialDistribution

• ExtremeValueDistribution

• GammaDistribution

• GeneralizedExtremeValueDistribution

• GeneralizedParetoDistribution

• HalfNormalDistribution

• InverseGaussianDistribution

• KernelDistribution

• LogisticDistribution

• LoglogisticDistribution

• LognormalDistribution

• NakagamiDistribution

• NegativeBinomialDistribution

• NormalDistribution

• PoissonDistribution

• RayleighDistribution

• RicianDistribution

• StableDistribution

• tLocationScaleDistribution

• WeibullDistribution

Output Arguments

nll — Negative loglikelihood

numeric value

Negative loglikelihood value for the data used to fit the distribution, returned as a numeric value.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).