Probability Distribution

Distribution of a statistical data set (or a population) is a statistical function or listing that describes all possible values (or intervals) and their occurrence in a random experiment with it’s associated probability.

Probability Distribution Is a mathematical function that maps the all possible outcomes of an random experiment with it’s associated probability. It depends on the Random Variable X , whether it’s discrete or continues.

Types of Distributions:

  • Continuous probability distributions: A continuous distribution describes the probabilities of the possible values of a continuous random variable. A continuous random variable is a random variable with a set of possible values (known as the range) that is infinite and uncountable.
    • Normal Distribution(Gaussian Distribution): It is a probability distribution with a bell-shaped curve where The peak always divides the distribution in half.
    • Exponential Distribution
    • Gamma Distribution
    • Chi-Square Distribution
    • Weibull Distribution
    • Laplace Distribution
    • Beta Distribution
    • T-Distributions
  • Discrete probability distributions
    • Bernoulli Distribution: It describes a single experiment that has ONLY two outcomes.
    • Binomial Distribution: is a method of calculating probabilities for experiments having a fixed number of trials(successes in repeated Bernoulli experiments).
      • The binomial distribution is used to estimate the total number of successes from n trials when only two possible outcomes are there: success and failure.
      • The name Binomial suggests two mutually exclusive outcomes of trials.
    • Multinomial Distribution
    • Negative Binomial Distribution: The negative binomial distribution is similar to the Poisson distribution but with two parameters instead of one: r and p. In such a case, the Poisson distribution is the limiting case of the negative binomial distribution.
    • Geometric Distribution: It’s similar to the Binomial distribution, however, the experiment continues until the S successes are achieved
    • Poisson Distribution: Poisson distribution is used to model the number of occurrences of a certain event given a very large number of observations and the probability of the desired event to occur in each observation is significantly smaller.
    • Hypergeometric Distribution: The hypergeometric distribution is related to the number of successes in a sequence of N trials from a finite population without replacement.
    • Beta-binomial distribution
  • Uniform Distribution: can be both Discrete and Continuous.
  • Joint Probability Distribution
  • Conditional Probability Distribution
  • Data distribution types based on:
    • Number of peaks:
      • Unimodal distribution
      • Bimodal distribution
      • Multimodal distribution
    • Symmetry(Uniform): A symmetric distribution with little skewness which has two sides that are mirror images of each other and can have a peak(for normal distribution) or a bottom for U-shaped graphs.
    • Skewness: A measure of the deviation of a random variable’s given distribution(assymetry in the data or variable distribution) from the normal distribution.
      • Negative skew: Distribution Concentrated in the right, left tail is longer.
      • Positive skew: Distribution Concentrated in the left, right tail is longer.


  • Joint distributions: Getting a distribution over some combination of several random variables.
  • Marginal distributions: If we have a joint distribution over some set of random variables, it is possible to obtain a distribution for a subset of them by “summing out” (or “integrating out” in the continuous case) the variables we don’t care about. I.e.