Data is one of the most important ingredients for building the best machine learning models. The more you know about the data, the better your machine learning model will be, as you will be able to depict the reason behind your model's performance. Probability is one of the most important mathematical tools that help in understanding data patterns.
In this article, we will not be only describing the theoretical aspects of probability but we will give you a sense of where those theoretical aspects will be used in ML.
So, let’s start without any further delay.
Originated from the “Games of Chance,” probability in itself is a branch of mathematics concerned about how likely it is that a proposition is true.
If we notice carefully, every daily-life phenomenon can only be of two types:
Mathematically, probability can be defined as :
If a random experiment has n > 0 mutually exclusive, exhaustive, and equally likely events and, if out of n, m such events are favorable ( m ≥ 0 and n ≥ m), then the probability of occurrence of any event E can be defined as
Let A and B are two events, A̅ is the complement of A, then.
Statistical Independence is a simple concept in probability theory that is all about the occurrence of any event.
Suppose two coins are to be tossed, then the probability of occurrences of the head or tail can be classified as:
When the probability of one event's occurrence depends on the probability of another event's occurrence, the scenario comes under statistical dependence.
If we have two events, A and B, then:
1. Conditional Probability is the probability of occurrence of an event A if event B has already occurred.
2. Joint Probability is the measure of two or more events happening at the same time. It can only be applied to situations where more than one observation can occur simultaneously, i.e., the probability of occurrence of event B at the same time when event A occurs.
3. Marginal Probability is obtained by summing up probabilities of all the joint events in which a simple event is involved.
If B1, B2, …, Bn are disjoint events and their union completes the entire sample space, then the probability of occurrence of an event A will be
P (A) = P (A ∩ B1) + P (A ∩ B2) + · · · + P (A ∩ Bn)
Let S be a sample space such that B1, B2, B3… Bn form the partitions of S and let A be an arbitrary event then,
𝑃(𝐵𝑖 ), 𝑖 = 1,2, …, 𝑛 are called the prior probabilities of occurrence of events.
𝑃(𝐵𝑘/A) is the posterior probability of 𝐵𝑘 when 𝐴 has already occurred.
In the context of the above image,
P(chill) := Probability that you are chilling out.
P(Netflix) := Probability that you are watching Netflix.
P(chill/Netflix) := Probability that you are chilling while watching Netflix.
P(Netflix/chill) := Probability that you will watch Netflix while chilling out.
Unlike algebraic variables, where the variable in an algebraic equation is unknown and calculated, random variables take on different values based on outcomes of any random experiment. It is just a rule that assigns a number to each possible outcome of an experiment.
Mathematically, a random variable is defined as a real function (X or Y or Z) of the elements of a sample space S to a measurable space E, i.e.,
Random variables are of two types:
Like frequency distribution, where all the observed frequencies that occurred as outcomes in a random experiment are listed, the Probability distribution listing probabilities of the events that actually occurred in a random experiment.
Probability Distribution defines the likelihood of possible values that a random variable can take.
Probability Distribution Function is of two types:
For Discrete Probability Distribution
For Continuous Probability Distribution:
Note:In the case of a discrete random variable, the probability at any point is not equal to zero, but in the case of a continuous random variable, the probability at one point is always zero, i.e., 𝑃(𝑋 = 𝑐) = 0. Thus, we conclude that in the case of a continuous random variable,
𝑃(𝑎 ≤ 𝑋 ≤ 𝑏) = 𝑃(𝑎 < 𝑋 < 𝑏) = 𝑃(𝑎 ≤ 𝑋 < 𝑏) = 𝑃(𝑎 < 𝑋 ≤ 𝑏)
If two or more random variables are given, and we want to determine the probability distribution, then joint probability distribution is used.
For Discrete random variables, Joint PMF is given by
For continuous random variables, Joint PDF is given by :
If the probability distribution is to be defined only on a subset of variables, the marginal probability distribution is used. It is beneficial if the probability is estimated on only a specific input variable set when given the other input values.
For discrete variables, the marginal distribution function of X is
For continuous variables, the marginal distribution function of X is
Sometimes, there are cases where we have to compute the probability of an event when a different event happens. This probability distribution is termed as conditional probability distribution.
For discrete random variables, the conditional distribution of X when Y is given:
For continuous variables, the conditional distribution function of X when Y is given:
Thus, it can be concluded that the conditional distribution function for any variable is equal to:
The expectation is a name given to a process of averaging when a random variable is involved.
If X be a discrete random variable defined by the values 𝑥1, 𝑥2, …, 𝑥𝑛 with the corresponding probabilities 𝑝1, 𝑝2, …, 𝑝𝑛 then the expectation of X or expected value of X is given by
If X be a continuous random variable with probability density function 𝑓(𝑥), then the expected value of X or expectation of X is given by
Variance: Variance means variability of random variables. It measures the average degree to which each number is different from the mean.
Mathematically, let X be any random variable (discrete or continuous) with 𝑓(𝑥) as its PMF or PDF, then the variance of X [denoted as var(X) or 𝜎2] is given by
Standard deviation [denoted as SD or 𝜎] is a statistic that looks at how far from the mean a group of numbers is by using the square root of the variance.
Mean (𝝁 𝒐𝒓 𝝈)
If 𝑥1, 𝑥2, …, 𝑥𝑛 are the 𝑛 variables then,
Or, if 𝑓1, 𝑓2, …, 𝑓n are the observed frequencies corresponding to the variables 𝑥1, 𝑥2, …, 𝑥𝑛 respectively then,
We know that Probability Distribution is the listing of the probabilities of the events that occurred in random experiments. Probability Distribution is of two types: i) Discrete Probability Distribution and ii) Continuous Probability Distribution. These can be further divided as:
Bernoulli Distribution: The Bernoulli Distribution is a discrete distribution where the random variable 𝑋 takes only two values 1 and 0 with probabilities 𝑝 and 𝑞 where 𝑝 + 𝑞 = 1. If 𝑋 is a discrete random variable, then 𝑋 is said to have Bernoulli distribution if
Here 𝑋 = 0 stands for failure and 𝑋 = 1 for success.
Binomial Distribution: The Binomial Distribution is an extension of Bernoulli Distribution. There are 𝑛 numbers of finite trails with only two possible outcomes, The Success with probability 𝑝 and The Failure with q probability. In this distribution probability of success or failure does not change from trail to trail, i.e., trails are statistically independent. Thus, mathematically a discrete random variable is said to be Binomial Distribution if
Poisson’s Distribution: The Poisson’s Distribution is limiting case of Binomial Distribution when the trails are infinitely large, i.e., 𝑛 → ∞, 𝑝, the constant probability of success of each trail is indefinitely small, i.e., 𝑝 → 0 and the mean of Binomial distribution [𝑛𝑝 = 𝜆] is finite.
Mathematically, a random variable 𝑋 is said to be a Poisson Distribution if it assumes only non-negative values and its PMF is given by
Normal Distribution: Normal Probability Distribution is one of the most important probability distributions mainly due to two reasons:
a) It is used as a sampling distribution.
b) It fits many natural phenomena, which include human
characteristics such as height, weight, etc.
The Normal Distribution is a probability function that describes how the values of a variable are distributed. It is a systematic distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions.
Mathematically it can be said that, if 𝑋 is a continuous random variable, then 𝑋 is said to have normal probability distribution if its PDF is given by
This can also be denoted by 𝑁(𝜇, 𝜎2).
When 𝜇 = 0 and 𝜎 = 1, the distribution becomes a special case called Standard Normal Distribution.
Any normal distribution can be converted into a Standard normal distribution using the formula.
Which is called ‘Standard Normal Variate’, and the 𝑍 here represents the Z-distribution.
Subscribe to get well designed content on data structure and algorithms, machine learning, system design, object orientd programming and math.
©2023 Code Algorithms Pvt. Ltd.
All rights reserved.