Exercise

# Maximizing Likelihood, Part 1

Previously, we chose the sample `mean`

as an estimate of the population model paramter `mu`

. But how do we know that the sample mean is the best estimator? This is tricky, so let's do it in two parts.

In Part 1, you will use a computational approach to compute the log-likelihood of a given estimate. Then, in Part 2, we will see that when you compute the log-likelihood for many possible guess values of the estimate, one guess will result in the maximum likelihood.

Instructions

**100 XP**

- Compute the
`mean()`

and`std()`

of the preloaded`sample_distances`

as the guessed values of the probability model parameters. - Compute the probability, for each
`distance`

, using`gaussian_model()`

built from`sample_mean`

and`sample_stdev`

. - Compute the
`loglikelihood`

as the`sum()`

of the`log()`

of the probabilities`probs`

.