quantlib-physics
Calculate Fisher Information
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:
- Assess quality of parameter estimates in models
- Determine optimal data collection strategies
- Evaluate information content for calibration
- Cramér-Rao bounds for risk measures
- Model sensitivity and parameter identifiability
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) [Tier: ENTERPRISE, Credits: 10]
POST
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