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@ -282,14 +282,12 @@ where the probability $P(\mathcal{G})$ that the random variables are normally di
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\begin{equation}
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\begin{equation}
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P(\mathcal{G}) = 1 - \chi^2_{\text{CDF}}(J,2)
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P(\mathcal{G}) = 1 - \chi^2_{\text{CDF}}(J,2)
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\end{equation}
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\end{equation}
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where $\chi^2_{\text{CDF}}(x,k)$ is the cumulative distribution function of the $\chi^2$-distribution with $k$ degrees of freedom.
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where $\chi^2_{\text{CDF}}(x,k)$ is the cumulative distribution function (CDF) of the $\chi^2$-distribution with $k$ degrees of freedom.
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As the number of samples is usually small, we use Student's $t$-distribution to estimate the statistical error.
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As the number of samples is usually small, we use Student's $t$-distribution to estimate the statistical error.
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The inverse of the cumulative distribution function of the $t$-distribution allows us to find how to scale the interval by a parameter
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The inverse of the cumulative distribution function of the $t$-distribution, $t_{\text{CDF}}^{-1}$, allows us to find how to scale the interval by a parameter
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\begin{equation}
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\begin{equation}
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%\beta = t_{\text{CDF}}^{-1} \left[
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\beta = t_{\text{CDF}}^{-1} \qty[
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%\frac{1}{2} \left( 1 + \frac{P( \Delta E_{\text{FCI}} \in [ \Delta E \pm \sigma ] \; | \; \mathcal{G}) }{P(\mathcal{G})}\right), n \right]
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\frac{1}{2} \qty( 1 + \frac{0.6827}{P(\mathcal{G})}), M ]
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\beta = t_{\text{CDF}}^{-1} \left[
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\frac{1}{2} \left( 1 + \frac{0.6827}{P(\mathcal{G})}\right), n \right]
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\end{equation}
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\end{equation}
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such that $P( \Delta E_{\text{FCI}}^{(m)} \in [ \Delta E_{\text{CIPSI}}^{(m)} \pm \beta \sigma ] ) = p$.
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such that $P( \Delta E_{\text{FCI}}^{(m)} \in [ \Delta E_{\text{CIPSI}}^{(m)} \pm \beta \sigma ] ) = p$.
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Only the last $M>2$ computed energy differences are considered. $M$ is chosen such that $P(\mathcal{G})>0.8$ and such that the error bar is minimal.
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Only the last $M>2$ computed energy differences are considered. $M$ is chosen such that $P(\mathcal{G})>0.8$ and such that the error bar is minimal.
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