# Log Likelihood Function

A likelihood method is a measure of how well a particular model fits the data; They explain how well a parameter (θ) explains the observed data. The logarithms of likelihood, the log likelihood function, does the same job and is usually preferred for a few reasons:

• The log likelihood function in maximum likelihood estimations is usually computationally simpler [1].
• Likelihoods are often tiny numbers (or large products) which makes them difficult to graph. Taking the natural (base e) logarithm results in a better graph with large sums instead of products.
• The log likelihood function is usually (not always!) easier to optimize.

For example, let’s say you had a set of iid observations x1, x2…xn with individual probability density function fX(x). Their joint density function is:

fX1, X2Xn(x1, x2…xn) = fX(x1) * fX(x2) * … * fX(xn) =

Where:

• Π = product (multiplication).

The log of a product is the sum of the logs of the multiplied terms, so we can rewrite the above equation with summation instead of products:

ln[fX(x1) * fX(x2) * … * fX(xn)] =

The above relationship leads directly to the log likelihood function[2]:

l(Θ) = ln[L(Θ)].

Although log-likelihood functions are mathematically easier than their multiplicative counterparts, they can be challenging to calculate by hand. They are usually calculated with software.

## Formulation of Log-Likelihood Function

The value of the parameter that maximizes the probability of observing data is called a maximum-likelihood estimate. Let’s say we had a set of data d made up of random variables D. We want to use the data to estimate a parameter θ.

## Uses of the Log-Likelihood Function

The log likelihood function frequently pops up in financial risk forecasting and probability and statistics—especially in regression analysis / model fitting. For example:

## References

[1] Robinson, E. (2016). Introduction to Likelihood Statistics. Retrieved April 16, 2021 from: https://hea-www.harvard.edu/AstroStat/aas227_2016/lecture1_Robinson.pdf
[2] Edge, M. (2021). Statistical Thinking from Scratch: A Primer for Scientists.