机器学习概率知识 probability
1- why we need to use probability in Machine Learning?
For instance, a finite amount of data sets exists within the financial transaction market, necessitating decisions made under insufficient information. Thus requiring the use of probability, we can quantify uncertainty.
2) - sample space (Ω omega)
sample space is a set of possible results or outcomes of experiment.
3) - sample outcomes
points omega ( ω) is called sample outcomes
4) - events
the subset of omega called event
2 - joint and conditional probability
Joint occurrence: For two events A and B, their joint probability refers to the likelihood that both these events occur simultaneously. The joint probability can be expressed as P(A and B) or P(A ∪ B).
conditional probability: determine the likelihood of event A occurring under the condition that event B has already taken place. (P(A|B) denotes this conditional probability, representing the likelihood that event A occurs given prior occurrence of event B.)
