#CDF TO PMF PDF#
The primary difference between PMF and PDF is, The probability mass function (PMF) is usually the primary component of defining a discrete probability distribution, but it differs from the probability density function (PDF) where it produces distinct outcomes (or in other words continuous variables). Probability Mass Function (PMF) and Probability Density Function (PDF) Let's consider X as a discrete random variable for the function, then the general formula for probability mass function is as follows: –įor all x belonging to range of X.
After the computation of all the probabilities, we can compute the probability distribution of the given random variable. Probability Mass Function, also called Discrete Density Function will allow us to find out the probability of getting a boost/interaction on a social media platform for each hour of the day i.e. Therefore, the random variable will take values ranging from 1 to 24. Now as there can be only 24 hours in the day. Let's understand this with the help of an example. Suppose we have to figure out at which hour of the day has more probability of getting a boost/interaction on social media platforms such as LinkedIn, provided we have some related data. We know that there are different types of distributions for discrete and continuous variables: A complete guide to the Probability Distribution and for every distribution, the formula of probability mass function varies. P(X) where X is a random variable is known as probability mass function, where a random variable can be: Probability mass function also called as probability function or frequency function is defined as a function that gives the probability that a discrete random variable is exactly equal to some value. Next, let's move forward and understand how we can use PMF to calculate the probabilities of a random variable whether it is discrete or continuous. Now, to calculate the probability of a random variable with its value equal to some value within the range, Probability Mass Function (PMF) is used. In our earlier post A complete guide to the Probability Distribution, we have developed a deep understanding of the different types of discrete and continuous possible probability distributions.
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