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Kolmogorov Axioms

The Kolmogorov Axioms form the foundational framework for probability theory, established by the Russian mathematician Andrey Kolmogorov in the 1930s. These axioms define a probability space (S,F,P)(S, \mathcal{F}, P)(S,F,P), where SSS is the sample space, F\mathcal{F}F is a σ-algebra of events, and PPP is the probability measure. The three main axioms are:

  1. Non-negativity: For any event A∈FA \in \mathcal{F}A∈F, the probability P(A)P(A)P(A) is always non-negative:

P(A)≥0P(A) \geq 0P(A)≥0

  1. Normalization: The probability of the entire sample space equals 1:

P(S)=1P(S) = 1P(S)=1

  1. Countable Additivity: For any countable collection of mutually exclusive events A1,A2,…∈FA_1, A_2, \ldots \in \mathcal{F}A1​,A2​,…∈F, the probability of their union is equal to the sum of their probabilities:

P(⋃i=1∞Ai)=∑i=1∞P(Ai)P\left(\bigcup_{i=1}^{\infty} A_i\right) = \sum_{i=1}^{\infty} P(A_i)P(⋃i=1∞​Ai​)=∑i=1∞​P(Ai​)

These axioms provide the basis for further developments in probability theory and allow for rigorous manipulation of probabilities

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