In the corner edge when CP( ) A = 0, we replace it with the number of singletons included in A if any, the number of unions of singletons included in A if any, and A itself. 

## 4. **A Class Of Dsm Conditioning Rules.**

In this way we can design a **class of DSm conditioning rules** taking into consideration not only masses, but also other parameters that might influence the decision-maker in calculating the subjective conditioning probability, and which is a generalization of Dempster's conditioning rule: 

$$m_{H D S m T c l a s s t}(X\mid A)=\frac{\sum_{Y\cap A=x}\frac{\alpha(Y)}{\beta(Y)}}{\sum_{Y\cap A=\phi}\frac{\alpha(Y)}{\beta(Y)}}$$

with α (Y) = α1(Y) ⋅ α2(Y) ⋅ … ⋅ αp(Y) , where all αi(Y), 1 ≤ i ≤ p, are parameters that Y is directly proportional to; and β(Y) = β 1(Y) ⋅ β 2(Y) ⋅ … ⋅ β r(Y), where all β j(Y), 1 ≤ j≤ r, are parameters that Y is inversely proportional to. 

## 5. **Examples Of Conditioning Rules.** Example 5.1.

Let m1(.) be defined on the frame {F = friend, E = enemy, N = neutral}, where the hypotheses F, 
E, N are mutually exclusive, in the following way (see the second row): 

| ϕ             | F   | E   | N   | F∪E   | F∪E∪N   | N∩ (F∪E)   |     |
|---------------|-----|-----|-----|-------|---------|------------|-----|
| m1            | 0   | 0.2 | 0.1 | 0.3   | 0.1     | 0.3        | 0   |
| m2            | 0   | 0   | 0   | 0     | 1       | 0          | 0   |
| m1+ m2        | 0   | 0.2 | 0.1 | 0     | 0.4     | 0          | 0.3 |
| mDCR(X|F∪E)   | 0   | 2/7 | 1/7 | 0     | 4/7     | 0          | 0   |
| mTBM(X|F∪E)   | 0.3 | 0.2 | 0.1 | 0     | 0.4     | 0          | 0   |
| mDSmT1(X|F∪E) | 0   | 0.2 | 0.1 | 0     | 0.7     | 0          | 0   |
| mDSmT2(X|F∪E) | 0   | 0.3 | 0.2 | 0     | 0.5     | 0          | 0   |
| Table 1       |     |     |     |       |         |            |     |
