thumb|455x455px| Figure 1: The left graph shows a probability density function. The right graph shows the cumulative distribution function. The value at in the cumulative distribution equals the area under the probability density curve up to the point . Absolutely continuous probability distributions can be described in several ways. The probability density function describes the infinitesimal probability of any given value, and the probability that the outcome lies in a given interval can be computed by integrating the probability density function over that interval. An alternative description of the distribution is by means of the cumulative distribution function, which describes the probability that the random variable is no larger than a given value (i.e., for some The cumulative distribution function is the area under the probability density function from to as shown in figure 1.
thumb|455x455px| Figure 1: The left graph shows a probability density function. The right graph shows the cumulative distribution function. The value at in the cumulative distribution equals the area under the probability density curve up to the point . Absolutely continuous probability distributions can be described in several ways. The probability density function describes the infinitesimal probability of any given value, and the probability that the outcome lies in a given interval can be computed by integrating the probability density function over that interval. An alternative description of the distribution is by means of the cumulative distribution function, which describes the probability that the random variable is no larger than a given value (i.e., for some The cumulative distribution function is the area under the probability density function from to as shown in figure 1