🏗️ ΘρϵηΠατπ🚧 (under construction)

sample space
A sample space Ω is a non-empty set
event
Given a sample space Ω, we say that any subset EΩ is an event
Probability Measure
Given a sample space Ω and a function P:Ω[0,1] , then we say that P is a probability measure if the following holds
  • EΩ,0P(E)1
  • P(Ω) =1
  • E1,E2,Ω such that E1,E2, are all pairwise disjoint P(i=1Ei)=i=1P(Ei)
Finite Pairwise Disjoint Probability
Suppose that A1,A2,,An are pairwise disjoint, then P(A1An)=i=1nP(Ai)
probability of a single point with equally likely outcomes is zero
Suppose that our sample space is [0,1] and there is some c[0,1] such that for any x[0,1], P({x})=c, then c=0
Union Superset yields Sum Inequality
Suppose that (En) is a sequence of sets and that Ei=1, then P(E)i=1P(Ei)
Random Variable
Suppose that Ω is a sample space. A random variable is a function X:Ω
Probability of a Random Variable being an Element of a Set
Suppose that X is a random variable and E some event , then we define P(XE):=P({aΩ:X(a)E})
Probability of a Random Variable being an Element of a Set using Inverse Image
P(XE)=P(X1(E)) where we're using the inverse image of X
Probability of a Random Variable being equal to An Element
Let yR, then we define P(X=y) as P(X{y})
conditional probability
Suppose that A,BΩ, with P(B)>0, then we define the conditional probability of A given B as
P(AB)=P(AB)P(B)
Uniform Probability Measure
Let Ω be a finite sample space, then we define the uniform probabilty measure as a probability measure such that given any event A we have P(A)=|A||Ω|

Note the division in the above always works since Ω is assumed non-empty

Increasing Sequence of Sets
Let (An) be a sequence of sets, then we say that it's increasing when A0A1A2...
A Sequence of Sets Increase to Another
Suppose that (An) is an increasing sequence of sets, then we say it increases to A when n=1An=A, and write (An)A