Chapter 16 Likelihood Approach

We will use different estimation techniques throughout this book, one of the main of which is Maximum Likelihood Estimate (MLE). The very rough idea of the approach is to maximise the chance that each observation in the sample follows a pre-selected distribution with specific set of parameters. In a nutshell, what we try to do when using likelihood for estimation, is fit the distribution function to the data. In order to demonstrate this idea, we start in a non-conventional way, with an example in R. We will then move to the mathematical side of the problem.

The software behind this book. The methods described here are implemented in the greybox package for R and Python, which are free and open source.

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