Although it’s usually thought of as an advanced topic, maximum likelihood estimation is key to understanding how many types of models are fit using software. Here we discuss the core ideas behind maximum likelihood estimation through simple examples, using the same distributions we saw in Chapter 4. Key terms: likelihood, log-likelihood, optimization.