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Good randomized sequential probability forecasting is always possible
Author(s) -
Vovk Vladimir,
Shafer Glenn
Publication year - 2005
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2005.00525.x
Subject(s) - minimax , von neumann architecture , frequentist probability , computer science , mathematical economics , probability distribution , mathematics , artificial intelligence , statistics , bayesian probability , operating system
Summary. Building on the game theoretic framework for probability, we show that it is possible, using randomization, to make sequential probability forecasts that will pass any given battery of statistical tests. This result, an easy consequence of von Neumann's minimax theorem, simplifies and generalizes work by earlier researchers.