General formulas and definitions

Formulas for A/B testing

The main metrics to perform A/B testing are described in [Stu15]. Let us consider two variants \(X_A\) and \(X_B\) for testing.

The error probability or probability of \(X_B > X_A\) is denoted as

\[P[X_B > X_A] = \int_{-\infty}^{\infty} \int_{x_A}^{\infty} f(x_A, x_B) \mathop{dx_B} \mathop{dx_A},\]

where \(f(x_A, x_B)\) is the joint probability distribution, under the assumption of independence, i.e. \(f(x_A, x_B) = f(x_A) f(x_B)\).

The expected loss function given a joint posterior is the expected value of the loss function. The loss function is the expected uplift lost by choosing a given variant. If variant \(X_B\) is chosen we have

\[EL(X_B) = \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} \max(x_A - x_B, 0) f(x_A, x_B) \mathop{dx_B} \mathop{dx_A}.\]

Other metrics also considered are the relative expected loss or uplift and credible intervals. A credible interval is a region which has a specified probability of containing the true value.

Formulas for Multivariate testing

Let us first introduce some properties of the distribution of the maximum of a set of independent random variables with support on the whole real line.

\[X_{max} = \max\{X_1, \ldots, X_n\}\]

The cumulative distribution function is

\[F_{X_{max}}(z) = P\left[\underset{i=1, \ldots, n}\max{X_i} \le z\right] = \prod_{i=1}^n P[X_i \le z] = \prod_{i=1}^n F_{X_i}(z),\]

where \(F_{X_i}(z)\) is the cdf of each random variable \(X_i\). The probability density functions is obtain after derivation

\[f_{X_{max}}(z) = \sum_{i=1}^n f_{X_i}(z) \prod_{j \neq i} F_{X_j}(z).\]

where \(f_{X_i}(z)\) is the pdf of each random variable \(X_i\).

The probability to beat all is defined as

\[P\left[X_i > \underset{j \neq i}\max{X_j}\right] = \int_{-\infty}^{\infty} f(x_i) \prod_{j \neq i} F_{X_j}(x_i) \mathop{dx_i}.\]

The expected loss function vs all is defined as

\[\mathrm{E}[\max(\underset{j \neq i}\max{X_j} - X_i, 0)]\]

Take \(Y = \underset{j \neq i}\max{X_j}\), then we have

\[\begin{split}EL(X_i) &= \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} \max(y - x_i, 0) f(y) f(x_i) \mathop{dx_i} \mathop{dy} \\ &= \int_{-\infty}^{\infty} \int_{-\infty}^y y f(y)f(x_i) \mathop{dx_i} \mathop{dy} - \int_{-\infty}^{\infty} \int_{-\infty}^y x_i f(y)f(x_i) \mathop{dx_i} \mathop{dy}\\ &= \int_{-\infty}^{\infty} y f(y) F_{X_i}(y) \mathop{dy} - \int_{-\infty}^{\infty} f(y) F^*_{X_i}(y) \mathop{dy},\end{split}\]

where \(F^*_{X_i}(y) = \int_{-\infty}^y x_i f(x_i) \mathop{dx_i}\).


[Stu15]C. Stucchio. Bayesian A/B Testing at VWO. Visual Web Optimizer, 2015. URL: