The first two rules are quite obvious. Eq. (31) is an extension of the third basic rule in the case two hypotheses are not mutually exclusive. In fact, if this is not case, the probability of is double counted and needs to be subtracted. Eq. (32) is also very intuitive, because either is true together with or with its opposite.

Formally, Eq. (33) follows from Eq. (32) and basic rule 4. Its interpretation is that the probability of any hypothesis can be seen as `weighted average' of conditional probabilities, with weights given by the probabilities of the conditionands [remember that and therefore Eq. (33) can be rewritten as

Eq. (34) and (35) are simple extensions of Eq. (32) and (33) to a generic `complete class', defined as a set of mutually exclusive hypotheses [ , i.e. ], of which at least one must be true [ , i.e. ]. It follows then that Eq. (35) can be rewritten as the (`more explicit') weighted average