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Changes between Version 2 and Version 3 of AcceptanceTerm

-              v2
+              v3
 === Definition ===
 In general, the probability that an event is accepted depends on the characteristics of the measured event, and not on the process that produced it. The measured probability density $\bar{P}(x,\alpha)$ can be related to the produced probability density $P(x,\alpha)$: %\[\bar{P}(x,\alpha)=Acc(x) P(x,\alpha)\]% where $ Acc(x)$ is the detector acceptance, which depends only on $ x $
+In general, the probability that an event is accepted depends on the characteristics of the measured event, and not on the process that produced it. The measured probability density $\bar{P}(x,\alpha)$ can be related to the produced probability density $P(x,\alpha)$: $\bar{P}(x,\alpha)=Acc(x) P(x,\alpha)$ where $ Acc(x)$ is the detector acceptance, which depends only on $ x $
 In the computation of the likelihood of the MatrixElement, this acceptance term induce the following term: %\[\int Acc(x) P(x,\alpha)dx\]%
+In the computation of the likelihood of the MatrixElement, this acceptance term induce the following term: $\int Acc(x) P(x,\alpha)dx$
 This could be estimated easily, by MC, as the number of accepted events on the number of generated events. %\[\frac{N_{accepted}}{N_{generated}}\]%
+This could be estimated easily, by MC, as the number of accepted events on the number of generated events. $\frac{N_{accepted}}{N_{generated}}$
 === How to compute Acceptance term with pythia/PGS. ===
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