  An equational axiomatisation of probability functions for one-dimensional
event spaces in the language of signed meadows is expanded with conditional
values. Conditional values constitute a so-called signed vector meadow. In the
presence of a probability function, equational axioms are provided for expected
value, variance, covariance, and correlation squared, each defined for
conditional values.
  Finite support summation is introduced as a binding operator on meadows which
simplifies formulating requirements on probability mass functions with finite
support. Conditional values are related to probability mass functions and to
random variables. The definitions are reconsidered in a finite dimensional
setting.
