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status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Axioms are self-evidently true statements that are unproven, but accepted. Axiomatic probability theory was developed by Andrey N. Kolmogorov, a prominent Russian mathematician, who was the founder of modern probability theory and believed the theory of probability as a mathematical discipline can and should be developed from axioms in exactly the same way as geometry and algebra. In the axiomatic deﬁnition of probability, the probability of the event \(A\), denoted by \(P (A)\), in the sample space \(S\) of a random experiment is a real number assigned to \(A\) that satisﬁes the following axioms of probability: Axiom I∶ \(P(A) \ge 0\) Axiom II∶ \(P(S)=1\) Axiom III∶ If \(A_1 , A_2, \ldots\) is a countable sequence of events such that \(A_i \cap A_j = \emptyset\) for all \(i \neq j\) where \(\emptyset\) is the null event, that is they are pairwise disjoint (mutually exclusive) events, then \(P \left( A_1 \cup A_2 \cup \ldots \right) = P (A_1) + P (A_2) + \ldots \tag{1.2}\) Note that these results do not indicate the method of assigning probabilities to the outcomes of an experiment, they merely restrict the way it can be done. These axioms satisfy the intuitive notion of probability. Axiom I of probability highlights that the probability of an event is nonnegative, i.e. chances are always at least zero. AxiomII of probability emphasizes that the probability of all possible outcomes is 1, i.e. the chance that something happens is always 100%. Axiom III of probability underscores that the total probability of a number of disjoint (mutually exclusive) events, i.e. nonoverlapping events, is the sum of the individual probabilities. Figure 1.3 shows the relationship among sample space, events, and probability.