example 11.62 Information in a counting experiment

Rests on

• depends_on example 11.58 Counting experiment ¶
  • depends_on definition 11.56 Maximum-likelihood estimator ¶
    • depends_on definition 11.54 Likelihood ¶
      • depends_on definition 11.53 Statistical model ¶
  • depends_on proposition 11.57 Invariance ¶
    • depends_on definition 11.56 Maximum-likelihood estimator ¶ ↺
    • proves proof ch:09-probability-statistics@proof-22 ¶
• depends_on lemma 11.61 Score identities ¶
  • depends_on definition 11.60 Score and Fisher information ¶
    • depends_on definition 11.53 Statistical model ¶ ↺
  • depends_on definition 11.59 Regularity conditions ¶
    • depends_on definition 11.60 Score and Fisher information ¶ ↺
    • depends_on definition 11.53 Statistical model ¶ ↺
  • proves proof ch:09-probability-statistics@proof-23 ¶
• depends_on theorem 11.64 Cramér–Rao inequality ¶
  • depends_on definition 11.60 Score and Fisher information ¶ ↺
  • depends_on definition 11.59 Regularity conditions ¶ ↺
  • depends_on lemma 11.63 Cauchy–Schwarz for random variables ¶
    • depends_on definition 11.11 Covariance and correlation ¶
      • depends_on definition 11.6 Moments, variance, standard deviation ¶
        • depends_on proposition 11.5 Transfer, linearity, monotonicity ¶
      • depends_on proposition 11.5 Transfer, linearity, monotonicity ¶ ↺
    • depends_on definition 11.6 Moments, variance, standard deviation ¶ ↺
    • proves proof ch:09-probability-statistics@proof-24 ¶
  • depends_on lemma 11.61 Score identities ¶ ↺
  • proves proof ch:09-probability-statistics@proof-25 ¶

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