In probability theory and applications, Bayes’ rule relates the odds of event
A
1
{\displaystyle A_{1}}
to event
A
2
{\displaystyle A_{2}}
, before (prior to) and informative post (posterior to) conditioning on another event
B
{\displaystyle B}
. When used as a modal verb of probability, “should” means that you assume something happened if everything is as you expect it to be. The present paper addresses the modelling of future average default probabilities. 56 Sklar’s find states that every multivariate cumulative distribution function
of a random vector
(
X
1
,
X
2
,
,
X
d
)
{\displaystyle (X_{1},X_{2},\dots ,X_{d})}
can be expressed in terms of its marginals
F
i
(
x
i
)
=
Pr
[
X
i
x
i
]
{\displaystyle F_{i}(x_{i})=\Pr[X_{i}\leq x_{i}]}
and
a copula
C
{\displaystyle C}
. .