Mixing
Fisher Information Matrix
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I am currently taking a module in predictive analytics and I have come across the Fisher Information Matrix.
Can somebody explain why this is so important, its use and why we need to calculate it.
Thanks in advance
statistics statistical-inference fisher-information
asked Mar 23 '18 at 14:10
user12321user12321
536
$endgroup$
add a comment |
$begingroup$
I am currently taking a module in predictive analytics and I have come across the Fisher Information Matrix.
Can somebody explain why this is so important, its use and why we need to calculate it.
Thanks in advance
statistics statistical-inference fisher-information
asked Mar 23 '18 at 14:10
user12321user12321
536
$endgroup$
add a comment |
$begingroup$
I am currently taking a module in predictive analytics and I have come across the Fisher Information Matrix.
Can somebody explain why this is so important, its use and why we need to calculate it.
Thanks in advance
statistics statistical-inference fisher-information
asked Mar 23 '18 at 14:10
user12321user12321
536
$endgroup$
I am currently taking a module in predictive analytics and I have come across the Fisher Information Matrix.
Can somebody explain why this is so important, its use and why we need to calculate it.
Thanks in advance
statistics statistical-inference fisher-information
statistics statistical-inference fisher-information
asked Mar 23 '18 at 14:10
user12321user12321
536
asked Mar 23 '18 at 14:10
user12321user12321
536
asked Mar 23 '18 at 14:10
user12321user12321
536
asked Mar 23 '18 at 14:10
user12321user12321
536
asked Mar 23 '18 at 14:10
user12321user12321
536
536
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
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The Fisher Information matrix is extremely important. It tells how much information one (input) parameter carries about another (output) value. So if you had a complete model of human physiology, you could use the Fisher information to tell how knowledge about 1) eating habits, 2) exercise habits, 3) sleep time, and 4) lipstick color affected a person's body mass. You'd find that the entries corresponding to the first three variables would be large but that the last would be zero.
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
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add a comment |
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Another perspective, the Fisher information matrix is very important because from its inverse we can estimate the variance and covariance of the parameter estimators of a likelihood function.
$Varleft(hat{beta_j}right)=I^{-1}left(hat{beta}_jright)$
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
The Fisher Information matrix is extremely important. It tells how much information one (input) parameter carries about another (output) value. So if you had a complete model of human physiology, you could use the Fisher information to tell how knowledge about 1) eating habits, 2) exercise habits, 3) sleep time, and 4) lipstick color affected a person's body mass. You'd find that the entries corresponding to the first three variables would be large but that the last would be zero.
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
$endgroup$
add a comment |
$begingroup$
The Fisher Information matrix is extremely important. It tells how much information one (input) parameter carries about another (output) value. So if you had a complete model of human physiology, you could use the Fisher information to tell how knowledge about 1) eating habits, 2) exercise habits, 3) sleep time, and 4) lipstick color affected a person's body mass. You'd find that the entries corresponding to the first three variables would be large but that the last would be zero.
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
$endgroup$
add a comment |
$begingroup$
The Fisher Information matrix is extremely important. It tells how much information one (input) parameter carries about another (output) value. So if you had a complete model of human physiology, you could use the Fisher information to tell how knowledge about 1) eating habits, 2) exercise habits, 3) sleep time, and 4) lipstick color affected a person's body mass. You'd find that the entries corresponding to the first three variables would be large but that the last would be zero.
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
$endgroup$
The Fisher Information matrix is extremely important. It tells how much information one (input) parameter carries about another (output) value. So if you had a complete model of human physiology, you could use the Fisher information to tell how knowledge about 1) eating habits, 2) exercise habits, 3) sleep time, and 4) lipstick color affected a person's body mass. You'd find that the entries corresponding to the first three variables would be large but that the last would be zero.
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
answered Mar 23 '18 at 15:12
David G. StorkDavid G. Stork
11.6k41533
11.6k41533
add a comment |
add a comment |
$begingroup$
Another perspective, the Fisher information matrix is very important because from its inverse we can estimate the variance and covariance of the parameter estimators of a likelihood function.
$Varleft(hat{beta_j}right)=I^{-1}left(hat{beta}_jright)$
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
$endgroup$
add a comment |
$begingroup$
Another perspective, the Fisher information matrix is very important because from its inverse we can estimate the variance and covariance of the parameter estimators of a likelihood function.
$Varleft(hat{beta_j}right)=I^{-1}left(hat{beta}_jright)$
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
$endgroup$
add a comment |
$begingroup$
Another perspective, the Fisher information matrix is very important because from its inverse we can estimate the variance and covariance of the parameter estimators of a likelihood function.
$Varleft(hat{beta_j}right)=I^{-1}left(hat{beta}_jright)$
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
$endgroup$
Another perspective, the Fisher information matrix is very important because from its inverse we can estimate the variance and covariance of the parameter estimators of a likelihood function.
$Varleft(hat{beta_j}right)=I^{-1}left(hat{beta}_jright)$
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
answered Jan 29 at 16:54
EL ComandanteEL Comandante
124
124
add a comment |
add a comment |
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