## Bernoulli likehood function

Let us define the Bernoulli likelihood function:

bernoulliLikelihood <- function(observedSample, piGreek) {
n <- length(observedSample)
(1 - piGreek)^(n - sum(observedSample)) * piGreek^sum(observedSample)
}

Suppose to have the following random sample:

tenTosses <- c(1, 0, 0, 1, 0, 0, 0, 0, 0, 1)

Likelihood values for three different values of the parameter:

bernoulliLikelihood(tenTosses, 0.1)
[1] 0.0004782969
bernoulliLikelihood(tenTosses, 0.3)
[1] 0.002223566
bernoulliLikelihood(tenTosses, 0.5)
[1] 0.0009765625

It is possible to compute the three likelihood values using only a function call:

bernoulliLikelihood(tenTosses, c(0.1, 0.3, 0.5))
[1] 0.0004782969 0.0022235661 0.0009765625

To plot the likelihood function we prepare a vector of values for the parameter spanning along the parameter space:

abscissa <- seq(0, 1, 0.01)

Then we compute and plot the likelihood function:

ordinate <- bernoulliLikelihood(tenTosses, abscissa)
plot(abscissa, ordinate, type = "l")

The relative (normalized) likelihood function is easily computed dividing by the maximum:

plot(abscissa, ordinate/max(ordinate), type = "l")

The relative likelihood function is useful to compare likelihood function for different sample sizes. Let us suppose to have a sample of 100 tosses with the same relative frequency of successes:

oneHundredTosses <- c(rep(0, 70), rep(1, 30))

Using the relative likelihood function we can compare the likelihood for the two samples on the same plot:

ordinate2 <- bernoulliLikelihood(oneHundredTosses, abscissa)
plot(abscissa, ordinate/max(ordinate), type = "l")
lines(abscissa, ordinate2/max(ordinate2), col = "red")

## Bernoulli log-likehood function

For computation it is convenient to use the logarith transformation of the likelihood function:

bernoulliLogLikelihood <- function(observedSample, piGreek) {
n <- length(observedSample)
(n - sum(observedSample)) * log(1 - piGreek) + sum(observedSample) * log(piGreek)
}

Here is the log-likelihood plot:

logOrdinate <- bernoulliLogLikelihood(tenTosses, abscissa)
plot(abscissa, logOrdinate, type = "l")

And here is the relative log-likelihood:

plot(abscissa, logOrdinate - max(logOrdinate), type = "l")

As for the likelihood function, the relative log-likelihood allows to easily compare the shape of the function for different sample sizes:

logOrdinate2 <- bernoulliLogLikelihood(oneHundredTosses, abscissa)
plot(abscissa, logOrdinate - max(logOrdinate), type = "l")
lines(abscissa, logOrdinate2 - max(logOrdinate2), col = "red")

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