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Probability Distributions useful in Machine Learning
Probability theory is all about measuring uncertainty. But before defining the uncertainty, we should have some object/event whose uncertainty we are talking about.
In this article I will talk about Random Variable, Probability Distribution and some of famous distributions of concern to a machine learning enthusiast.
What is Random Variable(RV)?
A variable that takes on different values randomly is called a random Variable. Now a RV can be discrete or continuous. Lets take an example of tossing two coins simultaneously. Then the RV, X= [HH, HT, TH, TT] denoted different states possible of X.
On its own, a RV is just a description of states thar are possible. It must be coupled with a probability distribution that specifies how likely each of these states are.
Probability Distribution
A probability distribution is a description of how likely a RV is to take on each of its possible states. Its generally denoted by “P(X)” with following 3 properties:-
- Domain of P = Possible states of X
- 0 ≤ P(x) ≤ 1, for all x in X
- Summation or Integration of P(x) = 1
