Probability Measure

Definition (Finitely Additive Probability Measure)

A finitely additive probability measure on (Ω,B0)(\Omega,\mathcal{B}_{0}), (where B0\mathcal{B}_{0} is an Algebra) is a map P:B0→[0,1]\mathbb{P}:\mathcal{B}_{0}\to[0,1]such that P(∅)=0,P(Ω)=1\mathbb{P}(\emptyset)=0,\quad\mathbb{P}(\Omega)=1andA,B∈B0,A∩B=∅  ⟹  P(A∪B)=P(A)+P(B)A,B\in\mathcal{B}_{0},A\cap B=\emptyset\implies \mathbb{P}(A\cup B)=\mathbb{P}(A)+\mathbb{P}(B)

Definition (*)

Let (Ω,F)(\Omega,\mathcal{F}) be a measurable space, a probability measure P\mathbb{P} is a measure such that P(Ω)=1\mathbb{P}(\Omega)=1

Proposition

For A∈FA\in\mathcal{F}: P(Ac)=1−P(a)\mathbb{P}(A^{c})=1-\mathbb{P}(a)

Proposition (Monotonicity of Probability Measure)

For A,B∈FA,B\in\mathcal{F}, if A⊆BA\subseteq B then P(A)≤P(B)\mathbb{P}(A)\le \mathbb{P}(B)

Proposition (Inclusion-Exclusion)

For 2 events, let A1,A2∈FA_1,A_2\in\mathcal{F}. P(A1∪A2)=P(A1)+P(A2)−P(A1∩A2)P(A_1\cup A_2)=P(A_1)+P(A_2)-P(A_1\cap A_2) For nn events, let A1,...,An∈FA_1,...,A_n\in\mathcal{F}: P(⋃i=1nAi)=∑k=1n(−1)k−1∑1≤i1<…<ik≤nP(Ai1∩…∩Aik)P(\bigcup^n_{i=1}A_i)=\sum^{n}_{k=1}(-1)^{k-1}\sum_{1\le i_1<\ldots< i_k\le n}P(A_{i_1}\cap \ldots\cap A_{i_k})

Proposition (Countable subadditivity)

For (An)n≥1⊆F(A_{n})_{n\ge 1}\subseteq \mathcal{F} we have that P(⋃n≥1An)≤∑n≥1P(An)\mathbb{P}\left( \bigcup_{n\ge 1} A_{n} \right)\le \sum_{n\ge 1}\mathbb{P}(A_{n})