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hdb
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The fisher information matrix for multivariate normal distribution is said at many places to be simplified as:
[tex]\mathcal{I}_{m,n} = \frac{\partial \mu^\mathrm{T}}{\partial \theta_m} \Sigma^{-1} \frac{\partial \mu}{\partial \theta_n}.\ [/tex]
even on
http://en.wikipedia.org/wiki/Fisher_information#Multivariate_normal_distribution"
I am trying to come up with the derivation, but no luck so far. Does anyone have any ideas / hints / references, how to do this?
Thank you
[tex]\mathcal{I}_{m,n} = \frac{\partial \mu^\mathrm{T}}{\partial \theta_m} \Sigma^{-1} \frac{\partial \mu}{\partial \theta_n}.\ [/tex]
even on
http://en.wikipedia.org/wiki/Fisher_information#Multivariate_normal_distribution"
I am trying to come up with the derivation, but no luck so far. Does anyone have any ideas / hints / references, how to do this?
Thank you
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