MACHINE LEARNING

Detailed explanation-1: -Every function has a probability distribution function. Explanation: Every continuous random variable x has a great Probability Density Function (PDF). The PDF can sometimes be greater than 1. This is in contrast to the discrete case.

Detailed explanation-2: -No, need not be. However, the cumulative distribution function (CDF), is always continuous (mayn’t be differentiable though) for a continuous random variable. For discrete random variables, CDF is discontinuous.

Detailed explanation-3: -Discrete probability distributions are probability distributions which assign a probability to each individual outcome. This is the full probability distribution of y. Continuous variables have a theoretically infinite continuum of values. They are functions that are defined, however, by their parameters.

Detailed explanation-4: -The probability distribution function is the integral of the probability density function. This function is very useful because it tells us about the probability of an event that will occur in a given interval (see Figures 1.5 and 1.6.