# Changes between Version 4 and Version 5 of MatrixElement

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Timestamp:
06/27/15 00:45:00 (4 years ago)
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 v4 The Matrix Element Method consist in minimizing a likelihood. The likelihood for N events is defined as $L(\alpha)=e^{-N \int \bar{P}(x,\alpha)dx} \prod_{i=1}^{N} \bar{P}(x_i;\alpha)$ The likelihood for N events is defined as $L(\alpha)=\prod_{i=1}^{N} \bar{P}(x_i;\alpha)$ The best estimate of the parameter $\alpha$ is obtained through a maximisation of the likelihood. It is common practice to minimize $-ln(L(\alpha))$ with respect to $\alpha$, $-ln (L)=-\sum_{i=1}^{N} ln(\bar{P}(x_i;\alpha)) + N \int \bar{P}(x,\alpha)dx$ 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 $x$. So the quantity that we have to minimize is $-ln (\tilde{L})}}}-\sum_{i=1}^{N} ln(P(x_i;\alpha)) + N \int Acc(x) P(x,\alpha)dx$ where the term $-\sum_{i=1}^N ln(Acc(x_i))$ has been omitted since it does not depend on $\alpha$. 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 $x$. So the quantity that we have to minimize is $-ln (\tilde{L}-\sum_{i=1}^{N} ln(P(x_i;\alpha)) + N \int Acc(x) P(x,\alpha)dx$ where the term $-\sum_{i=1}^N ln(Acc(x_i))$ has been omitted since it does not depend on $\alpha$. == Definition of the Weight ==