same weight to both data sets.

Estimation of parameter errors {#s:probdis}
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In order to obtain estimates of the errors on the parameters, we need their probability distribution prob($A\vert I$), where $A$ is the parameter under consideration and $I$ is the given background information. The most likely value of $A$ is then given by: $A_{\rm best}
={\rm max(prob(}A\vert I))$.

As we have to assume that all our parameters are coupled, we can only compute the probability for a given set of parameters. This probability is given by: $${\rm prob(}M_1,(m/M)_0,\beta,\gamma\vert I) \propto \exp(-\chi^2)$$ In a system with four free parameters $A,B,C$ and $D$ one can calculate the probability distribution of one parameter (e.g. $A$) if the probability distribution for the set of parameters is known, using marginalization: $$\begin{aligned}
{\rm prob(}A\vert I) &=& \int_{-\infty}^{\infty}{\rm prob(}A,B\vert I) {\rm d}B\notag\\
&=& \int_{-\infty}^{\infty}{\rm prob(}A,B,C,D\vert I) {\rm d}B {\rm d}C {\rm d}D\notag\end{aligned}$$

Once the probability distribution for a parameter is determined, one can assign errors based on the confidence intervals. This is the shortest interval that encloses a certain percentage $X$ of the area under the posterior probability distribution. For the 1-sigma error $X = 68\%$ while for the 2-sigma error $X = 95\%$. Assuming that 