# LaTeX editor 38

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A second, closely related point is that the *SR* model departs
from the *IS* model in abandoning the idea that a statistical
explanation of an outcome must provide information from which it
follows the outcome occurred with high probability. As the reader may
check, the statement of the *SR* model above imposes no such
high probability requirement; instead, even very unlikely outcomes
will be explained as long as the criteria for *SR* explanation
are met. Suppose that, in the above example, the probability of quick
recovery from strep, given treatment and the presence of a
non-resistant strain, is rather low (e.g., 0.2). Nonetheless, if the
criteria (i) — (iii) above — a homogneous partition with
correct probability values for each cell in the partition mathlab — are
satisfied, we may use this information to explain why *x*, who
had a non-resistant strain of strep and was treated, recovered
quickly. Indeed, according to the *SR* model, we may explain
why some *x* which is *A* is *B*, even if the
conditional probability of *B* given *A* and the cell
*C*_{i} to which *x* belongs
(*p*_{i} = P(*B|A.C*_{i})) is
*less* than the prior probability (*p* =
P(*B|A*)) of *B* in *A*. For example, if the
prior probability of quick recovery among all those with any form of
strep is 0.5 and the probability of quick recovery of those with a
resistant strain who are untreated is 0.1, we may nonetheless explain
why *y*, who meets these last conditions *(-T.R*) ,
recovered quickly (assuming he did) by citing the cell to which he
belongs ( the fact that he had the resistant strain and was
untreated), the probability LaTeX editor of recovery given that he falls in this
cell, and the other sort of information described above. More
generally, what matters on the *SR* model is not whether the
value of the probability of the explanandum-outcome is high or low (or
even high or low in comparison with its prior probability) but rather
whether the putative explanans cites all and only statistically
relevant factors and whether the probabilities it invokes are
correct. One consequence of this, which Salmon endorses while
acknowledging that many will regard it as unintuitive, is that on the
*SR* model, the same explanans *E* may explain both an
explanandum *M* and explananda that are inconsistent with
*M*, such as —*M*. For example, the same explanans
will explain both why a subject with strep and certain other
properties (e.g., *T* and *--R*) recovers quickly, if he
does, and also why he does not recover if he does not. By contrast, on
the *DN* or *IS* models, if *E* explains *M,
E* cannot also explain *--* *M*.

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