Bayesian probabilities are usually justified by the Cox theorems, that can be written this way:
Under some technical assumptions (continuity, etc, etc...), given a set P of objects A,B,C,ldots, with a boolean algebra defined over it with operations AwedgeB (and) and A|B (or) such that:
1) AwedgeB=BwedgeA
2) Awedge(BwedgeC)=(AwedgeB)wedgeC
3) A|(BwedgeC)=(A|B)wedge(A|C)
and a "valuation":
f:PrightarrowmathcalR
there is a strictly monotonic "regraduation function" R:mathcalRrightarrowmathcalR such that, for:
R(f(AwedgeB))=R(f(A))+R(f(B)) (sum rule)
and
R(f(A|B))=R(f(A))R(f(B)) (product rule)
This theorem allows one to show that any system designed to "evaluate" boolean expressions consistently with a single real number redunds in the laws of classical probability (this can be seen shortly here: arxiv:physics/0403089 and more thoroughly here: arxiv:abs/0808.0012)
Recently this has been extended for valuations of the type f:PrightarrowmathcalR2 in http://arxiv.org/abs/0907.0909 and they proved that there are just 5 canonical valuations compatible with the underlying Boolean algebra (one of them giving a complex field structure to the "valuation" field).
My question/proposal is: is it possible/interesting/feasible to classify at least a class of valuations of the type:
f:PrightarrowW
where W is a continuous manifold? If we retrict our attention to mathcalRn for example, is there, for each n, a set of canonical valuations to which all others can be reduced after a regraduation?
If this can be done, are those nice rules for inference in some sense? Are they useful as inference tools in specific situations?
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