In the first part of this paper, recalling a general dis- cussion on iterated conditioning given by de Finetti in the appendix of his book, vol. 2, we give a repre- sentation of a conditional random quantity X|HK as (X|H)|K. In this way, we obtain the classical formula P(XH|K) = P(X|HK)P(H|K), by simply using lin- earity of prevision. Then, we consider the notion of general conditional prevision P(X|Y ), where X and Y are two random quantities, introduced in 1990 in a paper by Lad and Dickey. After recalling the case where Y is an event, we consider the case of discrete finite random quantities and we make some critical comments and examples. We give a notion of coher- ence for such more general conditional prevision as- sessments; then, we obtain a strong generalized com- pound prevision theorem. We study the coherence of a general conditional prevision assessment P(X|Y ) when Y has no negative values and when Y has no positive values. Finally, we give some results on co- herence of P(X|Y ) when Y assumes both positive and negative values. In order to illustrate critical aspects and remarks we examine several examples.

### On general conditional random quantities

#### Abstract

In the first part of this paper, recalling a general dis- cussion on iterated conditioning given by de Finetti in the appendix of his book, vol. 2, we give a repre- sentation of a conditional random quantity X|HK as (X|H)|K. In this way, we obtain the classical formula P(XH|K) = P(X|HK)P(H|K), by simply using lin- earity of prevision. Then, we consider the notion of general conditional prevision P(X|Y ), where X and Y are two random quantities, introduced in 1990 in a paper by Lad and Dickey. After recalling the case where Y is an event, we consider the case of discrete finite random quantities and we make some critical comments and examples. We give a notion of coher- ence for such more general conditional prevision as- sessments; then, we obtain a strong generalized com- pound prevision theorem. We study the coherence of a general conditional prevision assessment P(X|Y ) when Y has no negative values and when Y has no positive values. Finally, we give some results on co- herence of P(X|Y ) when Y assumes both positive and negative values. In order to illustrate critical aspects and remarks we examine several examples.
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2009
general conditional random quantities; general conditional prevision assessments; generalized compound prevision theorem
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/20.500.11769/93186`
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