ΘρϵηΠατπ

sample space
A sample space Ω is a non-empty set
event
Given a sample space Ω, we say that any subset E⊆Ω 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
  • ∀E⊆Ω,0≤P(E)≤1
  • P(Ω) =1
  • ∀E1,E2,…⊆Ω such that E1,E2,… are all pairwise disjoint P(⋃i=1∞Ei)=∑i=1∞P(Ei)
Finite Pairwise Disjoint Probability
Suppose that A1,A2,…,An are pairwise disjoint, then P(A1∪…∪An)=∑i=1nP(Ai)
In the definition of a probability measure, for i∈{1,…,n} we set Ei=Ai and then for any j>n we set Ej=∅, then we can see that E1,E2,… is pairwise disjoint, and thus
P(A1∪A2∪…∪An) = P(A1∪A2∪…∪An∪∅∪∅∪…)
= P(⋃i=1∞Ei)
= ∑i=1∞P(Ei)
= ∑i=1nP(Ei)+P(∅)+P(∅)+P(∅)+…
= ∑i=1nP(Ei)+0+0+0+…
= ∑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
Suppose that c≠0, and note that P(Ω)=P([0,1])=∑x∈[0,1]P(x)=∑x∈[0,1]c, but note that no matter how small the value of c, since we are summing it uncountably many times with itself the sum always goes to infinity, but at the same time, P(Ω)=1 and thus we have a contradiction, so c=0.
Union Superset yields Sum Inequality
Suppose that (En) is a sequence of sets and that E⊆⋃i=1∞, then P(E)≤∑i=1∞P(Ei)

Define Fi=Ei∩E, we claim that E=⋃i=1∞Fi. Let x∈E, then there is some i∈ℕ1 such that x∈Ei, therefore x∈Ei∩E:=Fi, on the contrary suppose that a∈Fi for some i∈ℕ1, therefore a∈Ei∩E, so clearly a∈E, therefore E=⋂i=1∞Fi

Define G1:=F1 and set Gk:=Fk∩(⋂n=1k−1Fi)C for k≥2. Firstly note that the G's are pairwise disjoint, suppose that we have Gi,Gj where without loss of generatlity i<j, then we know that Gi⊆Fi, but at the same time we know that Gj⊆(⋂n=1jF)C, since Fi is part of that union, then when we take it's complement, clearly Gj cannot intersect with Fi and since Fi⊇Gi it cannot intersect with Gi as well.

Additionally we claim ⋃i=1∞Gi=⋃Fi=1∞. Let x∈⋃i=1∞Gi, so there exists some k∈ℕ1 such that x∈Gk, since Gk⊆Fk then we know x∈Fk that is there is some k such that x∈Fk so by definition x∈⋃i=1∞Fi. Moving the other way suppose that p∈⋃i=1∞Fi in that case there is some m∈ℕ1 so that p∈Fm, let I⊆ℕ1 be the collection of indices such that for every j∈I, p∈Fj, clearly I is non-empty since at least k∈I, therefore by the well ordering principle it has some least element l∈I, note that this means for any c∈[1…l−1] we have that p∉Fc, so by definition p∈Gl, so that we've shown p∈⋃i=1∞Gi as needed. We conclude that ⋃i=1∞Gi=⋃i=1∞Fi=E

P(E)=P(⋃i=1∞Gi)=∑i=1∞P(Gi)≤∑i=1∞P(Fi)≤∑i=1∞P(Ei) The first line is justified by the paragraph above, the second by the fact that the Gi's are disjoint, the third by the fact that Gi⊆Fi and the last because Fi⊆Ei.
Random Variable
Suppose that Ω is a sample space. A random variable is a function X:Ω→ℝ
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(X∈E):=P({a∈Ω:X(a)∈E})
Probability of a Random Variable being an Element of a Set using Inverse Image
P(X∈E)=P(X−1(E)) where we're using the inverse image of X
Probability of a Random Variable being equal to An Element
Let y∈ℝ, then we define P(X=y) as P(X∈{y})
conditional probability
Suppose that A,B⊆Ω, with P(B)>0, then we define the conditional probability of A given B as
P(A|B)=P(A∩B)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 A0⊆A1⊆A2⊆...
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=1∞An=A, and write (An)↗A