This book is in Open Review. I want your feedback to make the book better for you and other readers. To add your annotation, select some text and then click the on the pop-up menu. To see the annotations of others, click the button in the upper right hand corner of the page
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.