However, they can also be used to model more complex phenomena, such as the number of visitors to a website on a given day. Discrete probability distributions are often used to model events that can only have certain outcomes, such as the roll of a die or the flip of a coin. A discrete probability distribution assigns a probability to each discrete outcome of a random variable. The example shown above is an example of discrete probability distribution. For example, probability of number of heads occurring in 10 coin flips can be termed as discrete probability distribution. In other words, it helps to determine the likelihood that a random variable will take on a given value within well-defined range. Discrete Probability Distribution: Discrete probability distribution is a mathematical function that calculates the probabilities of outcomes of discrete random variables.Probability distributions are divided into two classes: The picture below represents the probability distribution of random variable X taking value 1 to 10. And the Y-axis will consist of values such as P(X=1), P(X=2), P(X=3), P(X=4), P(X=5). In the above example, the X axis will consist of values such as 1, 2, 3, 4 and 5. Probability distributions are often graphed as histograms, with the possibilities on the x-axis and the probabilities on the y-axis. Some of the most common examples include the uniform distribution, the normal distribution, and the Poisson distribution. There are many different types of distributions described later in this post, each with its own properties. Probability distribution for X would mean what is the probability that X can take values of 1, 2, 3, 4 and 5. That would mean that head will occur for 2 times. X can take values such as 1, 2, 3, 4 and 5. For example, lets take a random variable X as number of times “heads” occur when a coin is flipped 5 times. In other words, they provide a way of quantifying the chances of something happening. Probability distributions are a way of describing how likely it is for a random variable to take on different possible values. Different Types of Probability Distributions.
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