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Negative Loglikelihood Functions

Negative loglikelihood functions for supported Statistics and Machine Learning Toolbox™ distributions all end with like, as in explike. Each function represents a parametric family of distributions. Input arguments are lists of parameter values specifying a particular member of the distribution family followed by an array of data. Functions return the negative loglikelihood of the parameters, given the data.

To find maximum likelihood estimates (MLEs), you can use a negative loglikelihood function as an objective function of the optimization problem and solve it by using the MATLAB® function fminsearch or functions in Optimization Toolbox™ and Global Optimization Toolbox . These functions allow you to choose a search algorithm and exercise low-level control over algorithm execution. By contrast, the mle function and the distribution fitting functions that end with fit, such as normfit and gamfit, use preset algorithms with options limited to those set by the statset function.

Likelihoods are conditional probability densities. You can specify a parametric family of distributions by using a function f(x,a), where x and a represent a random variable and parameters, respectively. When a is fixed, the function is the probability density function (pdf) at x, f(x|a). When x is fixed, the function is the likelihood of the parameters a, f(a|x). The joint likelihood of the parameters over an independent random sample data set X is

L(a)=xXf(a|x).

Given X, MLEs maximize L(a) over all possible a. Numerical algorithms find MLEs that (equivalently) maximize the loglikelihood function, log(L(a)). The logarithm transforms the product of potentially small likelihoods into a sum of logs, which is easier to distinguish from 0 in computation. For convenience, Statistics and Machine Learning Toolbox negative loglikelihood functions return the negative of this sum because optimization algorithms typically search for minima rather than maxima.

Find MLEs Using Negative Loglikelihood Function

This example shows how to find MLEs by using the gamlike and fminsearch functions.

Use the gamrnd function to generate a random sample from a specific Gamma Distribution.

rng default;  % For reproducibility
a = [1,2];
X = gamrnd(a(1),a(2),1e3,1);

Visualize the likelihood surface in the neighborhood of a given X by using the gamlike function.

mesh = 50;
delta = 0.5;
a1 = linspace(a(1)-delta,a(1)+delta,mesh);
a2 = linspace(a(2)-delta,a(2)+delta,mesh);
logL = zeros(mesh); % Preallocate memory
for i = 1:mesh
    for j = 1:mesh
        logL(i,j) = gamlike([a1(i),a2(j)],X);
    end
end
 
[A1,A2] = meshgrid(a1,a2);
surfc(A1,A2,logL)

Search for the minimum of the likelihood surface by using the fminsearch function.

LL = @(u)gamlike([u(1),u(2)],X); % Likelihood given X
MLES = fminsearch(LL,[1,2])
MLES = 1×2

    0.9980    2.0172

Compare MLES to the estimates returned by the gamfit function.

ahat = gamfit(X)
ahat = 1×2

    0.9980    2.0172

The difference of each parameter between MLES and ahat is less than 1e-4.

Add the MLEs to the surface plot.

hold on
plot3(MLES(1),MLES(2),LL(MLES),'ro','MarkerSize',5,'MarkerFaceColor','r')
view([-60 40]) % Rotate to show the minimum

Consulte también

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