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Clojure for Data Science by Henry Garner

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Maximum likelihood estimation

On several occasions throughout this book, we've expressed optimization problems in terms of a cost function to be minimized. For example, in Chapter 4, Classification, we used Incanter to minimize the logistic cost function whilst building a logistic regression classifier, and in Chapter 5, Big Data, we used gradient descent to minimize a least-squares cost function when performing batch and stochastic gradient descent.

Optimization can also be expressed as a benefit to maximize, and it's sometimes more natural to think in these terms. Maximum likelihood estimation aims to find the best parameters for a model by maximizing the likelihood function.

Let's say that the probability of an observation x given model parameters ...

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