# transformation method

## Summary

The transformation method is a way of sampling from univariate probability distributions by sampling a uniform random variable and inverting the CDF.

## Context

This concept has the prerequisites:

- Monte Carlo estimation
- cumulative distribution function (The transformation method involves inverting the CDF.)

## Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

## Supplemental resources (the following are optional, but you may find them useful)

## -Free-

→ Machine learning summer school: Markov chain Monte Carlo (2009)

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 11.1.1, pages 526-528

## See also

- Other ways of sampling from a 1-D distribution include:
- rejection sampling
- slice sampling , an iterative method
- the polar coordinates trick for sampling from a Gaussian