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  3. Introduction to Linear Modeling in Python

Exercise

Maximizing Likelihood, Part 2

In Part 1, you computed a single log-likelihood for a single mu. In this Part 2, you will apply the predefined function compute_loglikelihood() to compute an array of log-likelihood values, one for each element in an array of possible mu values.

The goal then is to determine which single mu guess leads to the single maximum value of the loglikelihood array.

To get started, use the preloaded data sample_distances, sample_mean, sample_stdev and a helper function compute_loglikelihood().

Instructions

100 XP
  • Construct mu_guesses by taking values centered on sample_mean and spread by sample_stdev.
  • For each guess value mu_guess in mu_guesses, use compute_loglikelihood() for all sample_distances, holding sigma fixed at sample_stdev.
  • Find the maximum value in the loglikelihoods array and use its index to find the best_mu from our mu_guesses.
  • Print the best_mu and visualize it by plotting the loglikelihoods.