Calculates the Fisher information, which measures the amount of information that an observable random variable carries about an unknown parameter. Fisher information is fundamental in parameter estimation, providing the Cramér-Rao lower bound on estimator variance. Higher Fisher information means more precise parameter estimates are possible.
Use Cases:
Formula: I(θ) = E[(∂log f(X;θ)/∂θ)²]
Cramér-Rao bound: Var(θ̂) ≥ 1/I(θ)
Credits: 5 credits per request (Pro Tier)
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