Measure | Interpretation | Formula |

Probability (eg, of an event ) | A measure of the likelihood of an event. It takes a number between 0 (impossible) and 1 (certain). | |

Probability of a complementary event (eg, ) | A measure of the likelihood that an event will not occur. | |

Conditional probability (eg, of an event A given an event B) | A measure of the likelihood of an event (eg, A) given that another event (eg, B) has occurred. | |

Bayes’ theorem | Bayes’ theorem describes the probability of an event (eg,
) based on prior knowledge (eg, event B) of conditions that might be related to the event
. | |

Prior odds (eg, of an event A) | The odds in favour of A; the probability A will occur divided by the probability it will not occur. | |

Posterior odds (eg, of an event A given that B has occurred) | The odds of A in light of B; the probability A will occur given B has occurred divided by the probability A will not occur given that B has occurred. They inform us how odds of A have been updated given that B has occurred. | |

Likelihood ratio | The factor which updates prior odds in favour of an event (eg, A+) to posterior odds in favour of an event in the light of new information (eg, B). |

We use upper indices + and − to denote whether event A happens or not. For event B, we assume that it always happens and we omit the upper index.