### Fix doc and add guide description

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 ... ... @@ -9,24 +9,20 @@ def confidence_for_indicator_variable(x, n, alpha=0.05): The Clopper-Pearson interval method is used for estimating the confidence intervals. More info on confidence intervals --------------------------------- https://en.wikipedia.org/wiki/Confidence_interval https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Clopper-Pearson_interval Parameters ---------- x : int The number of successes. n : int The number of trials. alpha : float, optional alpha : :obj:float, optional The 1-confidence value that you want. For example, alpha should be 0.05 to obtain 95% confidence intervals. Returns ------- (float, float) Returns a tuple of (lower_bound, upper_bound) which (:obj:float, :obj:float) a tuple of (lower_bound, upper_bound) which shows the limit of your success rate: lower_bound < x/n < upper_bound ''' lower_bound = scipy.stats.beta.ppf(alpha / 2.0, x, n - x + 1) ... ...
 ... ... @@ -205,6 +205,25 @@ Both functions require that at least one probe item exists, which has no accordi >>> dir = bob.measure.detection_identification_rate(rr_scores, threshold = 0, rank=1) >>> far = bob.measure.false_alarm_rate(rr_scores, threshold = 0) 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 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. 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%. Plotting -------- ... ... @@ -436,4 +455,4 @@ that best suits you. .. _The Expected Performance Curve: http://publications.idiap.ch/downloads/reports/2005/bengio_2005_icml.pdf .. _The DET curve in assessment of detection task performance: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.4489&rep=rep1&type=pdf .. _openbr: http://openbiometrics.org .. _The Clopper-Pearson interval: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Clopper-Pearson_interval
 ... ... @@ -64,6 +64,13 @@ Generic bob.measure.rmse bob.measure.get_config Confidence interval ------------------- .. autosummary:: bob.measure.utils.confidence_for_indicator_variable Calibration ----------- ... ...
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