CDF FROM PDF PDF

One can be used to define a discrete probability distribution while the latter is used to defined a continuous probability distribution. The number you get from throwing a dice is an example of discrete random variable and the amount of rain falling in London is an example of a continuous random variable (the amount of water falling at any given time can be measured of course, but this number can be anywhere within a given range while in the case of the a dice, it can only take on a fix value, either 1, 2, 3, 4, 5 or 6).įigure 1: example of a probabilty mass function (top) and of a probability density function (bottom).

We also suggested that a random variable could either be discrete or continuous. In short, a probability distribution assigns a probability to each possible outcomes of a random experiment. In the previous chapters, we already introduced the concept of probability distribution.

CDF FROM PDF PDF

The Probability Distribution Function or PDF We are in fact likely to refer to this page quite often and even re-introduce those concepts in other lessons, to give them more visibility.

If you are serious about rendering and shading (from a programming point of view), this is a chapter we recommend you to read carefully. Because they are so important, they shouldn't be buried into a very long lesson on Monte Carlo methods, but we will use them in the next coming chapters and thus, they need to be introduced at this point in the lesson. The concepts of PDF ( probability density function) and CDF ( cumulative distribution function) is very important in computer graphics.

Cdf from pdf