On convergence of conditional probability measures

DOI10.2996/kmj/1138037002zbMath0599.60029OpenAlexW1986166708MaRDI QIDQ1080267

Akihiro Sugawara

Publication date: 1985

Published in: Kodai Mathematical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2996/kmj/1138037002

zbMATH Keywords

Kullback-Leibler informationtotal variation metricconditional probability measures given sample meansminimum I-divergencesSanov-type large deviation theorems

Mathematics Subject Classification ID

Large deviations (60F10) Convergence of probability measures (60B10) Measures of information, entropy (94A17) Statistical aspects of information-theoretic topics (62B10) Limit theorems for vector-valued random variables (infinite-dimensional case) (60B12)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Large deviations of the sample mean in general vector spaces
• Large deviation theorems for empirical probability measures
• A conditional law of large numbers
• I-divergence geometry of probability distributions and minimization problems
• Conditional expectation in an operator algebra. III.
• Conditional expectation in an operator algebra. IV. Entropy and information
• The asymptotic distribution of information per unit cost concerning a linear hypothesis for means of two given normal populations
• On Information and Sufficiency
• A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations

This page was built for publication: On convergence of conditional probability measures