Posted by: Juspreet51

Last Updated on: 04 Nov, 2021



One of the primary objkective of analytics is to measure the uncertainity associated with an event or key performance indicatorr. Axioms of probability and the concept of random variable are fundamental bulding blocks for analytics that are used for measuring uncertainity associated with the key performance indicators of importance of a business. Probability theory is the foundation on which descriptive and predictive analytics models are built.

Credits: Business Analytics: U Dinesh Kumar

Table of Contents

  1. Basics of Probability
  2. Axioms of Probability
  3. Bayes Theorem
  4. Difference Between Conditional Probability and Bayes Theorem
  5. Random Variable
  6. Distributions
    • Binomial Distribution
    • Poisson Distribution
    • Geometric Distribution
    • Continous Distribution
    • Uniform Distribution
    • Exponential Distribution
    • Normal Distribution
    • Chi-Squared Distribution
    • Student's t-Distribution
    • F-Distribution
  7. References

1. Basics of Probability

Probability is a metric for determining the likelihood of an event occurring, in terms of percentage. Many things are impossible to predict with 100% certainty. One can only predict the probability of such events occurring, i.e., how likely it is to occur.

  1. Random Experiment: Random Experiment is an experiment in which, the ouctome is not known with certainty. Predictive Analytics mainly deals with random experiment, where the outcome is not known, with certainity, e.g. attirition rate, customer churn, demand forecasting, etc.

  2. Sampel Space: Sample space is the universal set that consists of all possible outcomes of an experiment, represented by letter 'S', and individual outcomes are called as 'elementary events'.

    E.g. Outcome of a football match: S = {Win, Draw, Lose}
    Customer Churn: s = {Churn, Won't Churn}
    Life of a turbine balde in an aircraft engine: S = {X | X∈R, 0<=X<∞}

  3. Event: Event is the subset of sample space, and probability is calculate w.r.t. an event events_and_sample_space

  4. Probability Estimation: Probability of an event A, P(A), is given by: probability_estimation_formula

    E.g. in an organization of 1000 employees, if every year 200 employess leave their job, then the probability of attririon of an employee per annum is 200/1000 = 0.2
    * If the probability for an event to happen is p and the probability for it to fail is q, then p + q = 1

  5. Algebra of Events: For multiple events from a sample space, we can apply and use following algebric expressions, to either directly achieve or to derive results:

    • Addition Rule: The probability that events A or B will occur is given by
      P(A ∪ B) = P(A) + P(B) – P(A ∩ B)
      P(A or B) = P(A) + P(B) – P(A and B)
      If A, B and C are mutually exclusive events, then P(A or B or C) = P(A) + P(B) + P(C)
    • Multiplication Rule: The probability that two events A and B will occur in sequence is
      P(A∩B) = P(A) × P(B/A) = P(B) × P(A/B)
      P(A and B) = P(A) × P(B/A) = P(B) × P(A/B)
      If A, B and C are independent events, then
      P(A and B and C) = P(A) × P(B) × P(C)
    • Commulative Rule:
      A ∪ B = B ∪ A, and A ∩ B = B ∩ A
    • Associative Rule:
      (A ∪ B) ∪ C = A ∪ (B ∪ C), and
      (A ∩ B) ∪ C = A ∩ (B ∩ C)
    • Distributive Rule:
      A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C), and
      A ∩ (B ∪ C) = (A ∩ B) ∪ (A ∩ C)

Axioms of Probability

  1. Probability of an event always lies between 0 & 1
  2. Probability of the universal set S is always 1
  3. For any event A(e.g. probability of observing a fraudlent transaction), probability of the complementary event(e.g. probability of observing a non-fraudlent transaction), written as A C is given by:
    P(Ac) = 1 - P(A)
  4. The probability that either event A or B occurs, or both occurs, is given by
    P(A ∪ B) = P(A) + P(B) - P(A ∩ B)
  5. Marginal Probability: Marginal Probability is simply a probability of an event X, denoted by P(X), without any conditions marginal_probability
  6. Joint Probability: Joint probability is the likelihood that two events will happen at the same time. It's the probability that event X occurs at the same time as event Y. joint_probability
    Conditions being:
    • events A and B must happen at the same time
    • events A and B must be independent (non-interfering) of each other
    e.g. throwing two dices


    The word joint comes from the fact that we’re interested in the probability of two things happening at once, like here, male & baseball
    So, according to our above statd Joint Probability forumula joint_probability_example

  7. Conditional Probability is known as the possibility of an event or outcome happening, based on the existence of a previous event or outcome
    If events A & B are events in a sample space, then the conditional probability of the event B, given that the event A has already happened, is denoted by P(B|A), and is defined as: conditional_probability

    For example, we might expect the probability that it will rain tomorrow (in general) to be smaller than the probability it will rain tomorrow given that it is cloudy today. This latter probability is a conditional probability, since it accounts for relevant information that we possess.

    Mathematically, computing a conditional probability amounts to shrinking our sample space to a particular event. So in our rain example, instead of looking at how often it rains on any day in general, we "pretend" that our sample space consists of only those days for which the previous day was cloudy.

Application of Probability

Association Rule Learning: Market Basket Analysis is used frequently by retailers, to predict items frequenlty bought together by the customers, to improve thier product promotions. And, Association Rule Learning is a method of finding associations between different entities.
And the Strength-of-Association between 2 items can be measured using Support, Confidence & Lift
  1. Support: support_formula
  2. Confidence: confidence_formula
  3. Lift: lift_forumla

Bayes Theorem

Bayes Theorem can be understood as the description of the probability of any event which is obtained by prior knowledge about the event

We can say that Bayes’ theorem can be used to describe the conditional probability of any event where we have

  • data about the event
  • prior information about the event
  • with the prior knowledge about the conditions related to the event

As per Conditional Probability, we know that bayes_derivation
  • Prior Probability: In the formula of the Bayes theorem, P(A) is the Prior Probability
    According to Bayesian statistical inference we can define the Prior Probability or Prior, of an uncertain quantity as the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account
    For example, the relative proportions of voters, who will vote for a particular politician in a future election
    P(A) expresses one’s belief of occurrence of A before any evidence is taken into account
  • Likelihood Function: P(B|A) is the likelihood function which can be simply called the likelihood
    It can be defined as the probability of B when it is known that A is true
    Numerically it is the support value where the support to proposition A is given by evidence B
  • Posterior Probability: P(A|B) is the posterior probability or the probability of A to occur given event B already occurred

A great article on Monty Hall Problem, which is solved using Bayes Theorem. Go through it once before proceeding further about Random Variables and Distribution Functions

Relationship Between Conditional Probability and Bayes Theorem

** The numerator, i.e. P(B|A)*P(A) says that, both the event has occured, i.e. Event (A) & Event (B|A)
And from the concept of Conditional Probability, the denominator P(B) says, P(Numerator|B), i.e. Probability of Numerator happening, give event B has already happened

Random Variable

Random Variable: A random variable is a variable whose value depends on unknown events. We can summarize the unknown events as "state", and then the random variable is a function of the state random_variable
Credits: Paul
A variable is a symbol that represents some quantity, whereas, a random variable is a value that follows some probability distribution.
Discrete Random Variable: If a random variable can assume only a finite, or countably infinite set of values, then it is called as a Discrete Random Variable
E.g. 5 pencils, 100 vehicles, 10,000 user registraions
Continous Random Variable: If a rancom variable can take a value from an infinite set of values, it is called as Continous Random Variable
E.g. length of pencil (7cm or 7.32cm or 7.321456894123cm)

Functions to consider when we have
  • Discrete Random Variable: Probability Mass Function
  • Continous Random Variable: Probability Density Function
  • Both cases: Cumulative Distribution Function
The term probability relates is to an event and probability distribution relates is to a random variable.
A great video about probability distribution:

It is a convention that the term probability mass function refers to the probability function of a discrete random variable and the term probability density function refers to the probability function of a continuous random variable

  1. PMF: When the random variable is discrete, probability distribution means, how the total probability is distributed over various possible values of the random variable
    Consider the experiment of tossing two unbiased coins simultaneously. Then, sample space S associated with this experiment is:
    S = {HH, HT, TH, TT}
    If we define a random variable X as: the number of heads on this sample space S, then
    X(HH) = 2, X(HT) = X(TH)=1, X(TT) = 0
    The probability distribution of X is then given by P(x) = x/n
    P(2) = 1/4; P(1) = 2/4; P(0) = 1/4
    Which could be represented as in pmf_visualization

    For a discrete random variable, we consider events of the type {X=x} and compute probabilities of such events to describe the distribution of the random variable. This probability, P{X=x}=p(x) is called the probability mass function

  2. PDF: A continuous random variable is described by considering probabilities for events of the type (a<X<b). The probability function of a continuous random variable is usually denoted by f(x) and is called a probability density function pdf_formula
    It is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would be close to that sample. pdf_visualization

  3. CDF: Cumulative Distribution Function, F(Xi), is the probability that the random variable X takes values less than or equal to Xi, i.e. cdf


The distribution is simply, the assignment of probabilities, to sets of possible values of the random variable. And, a probability distribution is a statistical function, that describes all the possible values and likelihoods that a random variable can take within a given range

  • Bernoulli Distribution: Any event, which has 1 trial with 2 possible events, follows Bernoulli Distribution, e.g. Coin Flip bernouli_distribution
    Credits: Wikipedia

  • Binomial Distribution: Binomial Distribution helps in getting number of times we expect to get a specific outcome. As, its graph represents our likelihood of attaining our desired outcome, a specific number of times
    Binomial probability mass function and normal probability density function approximation for n = 6 and p = 0.5
    Credits: Wikipedia

    In order for a random variable to be considered for Binomial Distribution, it needs to satisfy following conditions:
    • Only 2 unique outomes possible, i.e. Bernoulli Trials
    • Objective: probability of getting k successes, out of n trials
    • Each trial is a "yes-no asking" independent event
    • Probability P is constant, and doesn't varies between trials (probability of getting tails doesn't changes after some n-p trials)
    PMF of Binomial Distribution cdf Few good examples include binary classifications like churn and wont churn, fraud transaction & non-fraud transactions, loan emi default and no default, etc

  • Poisson Distribution: Sometimes we are interested in the count of events, e.g. number of orders in an ecommerce website, number of cancelled orders, number of customer complaints in a telecom company, etc. In such cases, we need probaility distribution of number of events, i.e. Poisson Distribution.
    Credits: Wikipedia

    E.g. the number of mails you receive monthly, are countable and independent of each other.
    It is a poisson distribution event, as it deals with the frequency at which an event occurs in a specific interval, instead of the probability of the event, it wants to know how often an event occurs, for a period of time
    Another example, firefly lighting up 3 times in 10seconds
  • Geometric Distribution: Sometimes, we're more interested to discover about the number of failures between 2 success in Bernoulli Distribution, e.g. as in case of duration between 2 purchse of visit to the store. Here, if each day is 1 trial, and the outcome is purchased & not-purchsaed, then each point in x-axis of the bernouli graph would be a unique single day, which could be either purchased or not-purchsaed. And Geometric Distribution will help us in knowing what is the duration between 2 purchases
    I.e. Geometric Distribution si the number of Bernoulli Trials required to achieve success. It represents a random experiment in which the random variable predicts the number of failures before the success.
    A great explanation of Geometric Distribution: geometric_distribution
    Credits: K. A. Buhr

  • Continous Distribution:
  • Uniform Distribution: In statistics, uniform distribution refers to a type of probability distribution in which all outcomes are equally likely. E.g. A deck of cards has within it uniform distributions because the likelihood of drawing a heart, a club, a diamond, or a spade is equally likely. A coin also has a uniform distribution because the probability of getting either heads or tails in a coin toss is the same.
    There are two types of uniform distributions: discrete and continuous. The possible results of rolling a die provide an example of a discrete uniform distribution : it is possible to roll a 1, 2, 3, 4, 5, or 6, but it is not possible to roll a 2.3, 4.7, or 5.5. Therefore, the roll of a die generates a discrete distribution with p = 1/6 for each outcome. There are only 6 possible values to return and nothing in between[1]. discrete_uniform_distribution
    Credits: Wikipedia

    Some uniform distributions are continuous rather than discrete. An idealized random number generator would be considered a continuous uniform distribution . With this type of distribution, every point in the continuous range between 0.0 and 1.0 has an equal opportunity of appearing, yet there is an infinite number of points between 0.0 and 1.0[2]. continous_uniform_distribution
    Credits: Wikipedia

  • Exponential Distribution: In Exponential Distribution, the events occur continuously and independently at a constant average rate. The exponential distribution, Erlang distribution, and chi-square distribution are special cases of the gamma distribution exponential_distribution

  • Normal Distribution: Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve[3]. exponential_distribution
    A normal random variable with mean = 0 & standard deviation = 1 is called as Standard Normal Variable, and usually represented by Z
  • Chi-Squared Distribution: When k independent standard normal random variable are squared and summed together, the non-parametric distribution obtained is called as Chi-Square Distribution with K-Degrees of Freedom.
    Used to check the relationships between categorical variables, to study the sample variance where the underlying distribution is normal, and most importantly, to conduct a The chi-square test (a goodness of fit test)
  • Student's t-Distribution: The distribution arise when estimating the mean of a normally distributed population in situations where the sample size is small and the population's standard deviation is unknown exponential_distribution
  • F-Distribution: is a continuous probability distribution that arises frequently as the null distribution of a test statistic, most notably in the analysis of variance (ANOVA) and other F-tests.
    Null Distribution is the probability distribution of the test statistic when the null hypothesis is true