Fisher's formalism - which conditions to get equivalence between Fisher matrix and inverse of covariance...












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I am currently studying Fisher's formalism as part of parameter estimation.



From this documentation :



doc fisher



They that Fisher matrix is ​​the inverse matrix of the covariance matrix. Initially, one builds a matrix "full" that takes into account all the parameters.



Which are the conditions to say that Fisher matrix is the inverse of covariance matrix ?



I saw that Cramér-Rao could be implied into this criteria but I don't know how to use it to demonstrate or not this equivalence between inverse(covariance) = Fisher.



Any help is welcome






matrices covariance projection-matrices fisher-information







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$endgroup$

















    0












    $begingroup$


    I am currently studying Fisher's formalism as part of parameter estimation.



    From this documentation :



    doc fisher



    They that Fisher matrix is ​​the inverse matrix of the covariance matrix. Initially, one builds a matrix "full" that takes into account all the parameters.



    Which are the conditions to say that Fisher matrix is the inverse of covariance matrix ?



    I saw that Cramér-Rao could be implied into this criteria but I don't know how to use it to demonstrate or not this equivalence between inverse(covariance) = Fisher.



    Any help is welcome






    matrices covariance projection-matrices fisher-information







    share|cite|improve this question











    $endgroup$















      0












      0








      0





      $begingroup$


      I am currently studying Fisher's formalism as part of parameter estimation.



      From this documentation :



      doc fisher



      They that Fisher matrix is ​​the inverse matrix of the covariance matrix. Initially, one builds a matrix "full" that takes into account all the parameters.



      Which are the conditions to say that Fisher matrix is the inverse of covariance matrix ?



      I saw that Cramér-Rao could be implied into this criteria but I don't know how to use it to demonstrate or not this equivalence between inverse(covariance) = Fisher.



      Any help is welcome






      matrices covariance projection-matrices fisher-information







      share|cite|improve this question











      $endgroup$




      I am currently studying Fisher's formalism as part of parameter estimation.



      From this documentation :



      doc fisher



      They that Fisher matrix is ​​the inverse matrix of the covariance matrix. Initially, one builds a matrix "full" that takes into account all the parameters.



      Which are the conditions to say that Fisher matrix is the inverse of covariance matrix ?



      I saw that Cramér-Rao could be implied into this criteria but I don't know how to use it to demonstrate or not this equivalence between inverse(covariance) = Fisher.



      Any help is welcome






      matrices covariance projection-matrices fisher-information




      matrices covariance projection-matrices fisher-information






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Jan 30 at 15:53







      youpilat13

















      asked Jan 26 at 22:56









      youpilat13youpilat13

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