Seconder of the vote of thanks to Waudby-Smith and Ramdas and contribution to the Discussion of ‘Estimating means of bounded random variables by betting’
该文提出一个基于博弈论的新框架，通过将置信区间构建转化为赌博游戏，推导出随机变量均值的时间一致置信区间，对统计推断和概率论基础研究有重要价值。
The paper proposes an elegant new framework for deriving time-uniform confidence intervals for the mean of a sequence of random variables. The proposed technique is based on a game-theoretic view of statistical inference, rephrasing the problem of building confidence intervals as identifying a set of coin-betting games with a certain "plausibility" property: if one can get "implausibly rich" in a game by betting on the mean being lower (resp. higher) than a certain threshold, then the mean must be higher (resp. lower) than said threshold. A confidence interval can then be derived as the interval between the lowest and highest values that cannot be ruled out as "implausible". This game-theoretic view extends the pioneering work of Ville (1939) and Shafer and Vovk (2019) who used similar betting games to lay an alternative set of foundations of probability theory that is free from measure theory.