### Math support for conf inter doc

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 ... ... @@ -208,18 +208,22 @@ Both functions require that at least one probe item exists, which has no accordi Confidence interval ------------------- A confidence interval for parameter x consists of a lower estimate L, and an upper estimate U, such that the probability of the true value being within the interval estimate is equal to \alpha. For example, a 95% confidence interval (i.e. \alpha = 0.95) for a parameter x is given by [L, U] such that Prob(x∈[L,U]) = 95%. The smaller the test size, the wider the confidence interval will be, and the greater alpha, the smaller the confidence interval A confidence interval for parameter :math:x consists of a lower estimate :math:L, and an upper estimate :math:U, such that the probability of the true value being within the interval estimate is equal to :math:\alpha. For example, a 95% confidence interval (i.e. :math:\alpha = 0.95) for a parameter :math:x is given by :math:[L, U] such that .. math:: Prob(x∈[L,U]) = 95% The smaller the test size, the wider the confidence interval will be, and the greater :math:\alpha, the smaller the confidence interval will be. The Clopper-Pearson interval_, a common method for calculating confidence intervals, is function of the number of success, the number of trials and confidence value \alpha is used as :py:func:bob.measure.utils.confidence_for_indicator_variable. value :math:\alpha is used as :py:func:bob.measure.utils.confidence_for_indicator_variable. It is based on the cumulative probabilities of the binomial distribution. This method is quite conservative, meaning that the true coverage rate of a 95% Clopper–Pearson interval may be well above 95%. ... ...
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