Data Science Texts

Discover what you don't know, and attack your weaknesses!

NB: We may earn a commission if you buy something via an affiliate link.

Density Estimation

Strongly Recommended Prerequisites

Recommended Prerequisites

Last Updated: 8/29/2021

Density estimation is the study of optimal methods for estimating probability densities. It comes in two flavors: parametric and nonparametric. Parametric estimation is solved in the usual point estimation way. Nonparametric density estimation is much less straightforward. A classic example of a nonparametric density estimate is the histogram, which like all density estimates is both a useful graphical summary of data and an important object for statistical analysis.

Recommended Books

  1. Multivariate Density Estimation: Theory, Practice, and Visualization

    David W. Scott

    Book image of Multivariate Density Estimation: Theory, Practice, and Visualization.
    Check it out on Amazon!

    Key Features


    We like this book's didactic approach to nonparametric density estimation. It starts with the histogram and develops it into kernel density estimation and multivariate density estimation. The text is very readable, and there are a lot of bonus topics, such as regression and mode hunting. The downside of this book is that there are no solutions to the exercises and no code examples. It's fairly easy to find code in many languages that does basic nonparametric density estimation though.

  2. Density Estimation for Statistics and Data Analysis

    B.w. Silverman

    Check it out on Amazon!

    Key Features


      This monograph is the classic text on density estimation. There are no exercises, and it's fairly short. However, if you do a lot of density estimation you'll benefit from this master's perspective.