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The log-likelihood is, as the term suggests, the natural logarithm of the likelihood.

In turn, given a sample and a parametric family of distributions (i.e., a set of distributions indexed by a parameter) that could have generated the sample, the likelihood is a function that associates to each parameter the probability (or probability density) of observing the given sample.

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The following elements are needed to rigorously define the log-likelihood function:

Given all these elements, the log-likelihood function is the function [eq7] defined by[eq8]


The typical example is the log-likelihood function of a sample that is made up of independent and identically distributed draws from a normal distribution.

In this case, the sample $xi $ is a vector[eq9]whose entries [eq10] are draws from a normal distribution. The probability density function of a generic draw $x_{i}$ is[eq11]where mu and sigma^2 are the parameters (mean and variance) of the normal distribution.

With the notation used in the previous section, the parameter vector is[eq12]The parametric family being considered is the set of all normal distributions (that can be obtained by varying the parameters mu and sigma^2).

In order to stress the fact that the probability density depends on the two parameters, we write[eq13]

The joint probability density of the sample $xi $ is [eq14]because the joint density of a set of independent variables is equal to the product of their marginal densities (see the lecture on Independent random variables).

The likelihood function is[eq15]

The log-likelihood function is


How the log-likelihood is used

The log-likelihood function is typically used to derive the maximum likelihood estimator of the parameter $	heta $. The estimator $widehat{	heta }$ is obtained by solving[eq17]that is, by finding the parameter $widehat{	heta }$ that maximizes the log-likelihood of the observed sample $xi $. This is the same as maximizing the likelihood function [eq18] because the natural logarithm is a strictly increasing function.

Why the log is taken

One may wonder why the log of the likelihood function is taken. There are several good reasons. To understand them, suppose that the sample is made up of independent observations (as in the example above). Then, the logarithm transforms a product of densities into a sum. This is very convenient because:

More examples

More example of how to derive log-likelihood functions can be found in the lectures on:

More details

The log-likelihood and its properties are discussed in a more detailed manner in the lecture on maximum likelihood estimation.

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