Beta distribution

The probability density function of the beta distribution \(\mathcal{B}(\alpha, \beta)\) with two shape parameters \(\alpha, \beta > 0\), for \(x \in [0, 1]\), is defined by

\[f(x; \alpha, \beta) = \frac{x^{\alpha - 1} (1-x)^{\beta - 1}}{B(\alpha, \beta)},\]

and the cumulative distribution function is

\[F(x; \alpha, \beta) = I_x(\alpha, \beta),\]

where \(B(\alpha, \beta)\) is the beta function and \(I_x(\alpha, \beta)\) is the regularized incomplete beta function. The expected value and variance are as follows

\[\mathrm{E}[X] = \frac{\alpha}{\alpha + \beta}, \quad \mathrm{Var}[X] = \frac{\alpha \beta}{(\alpha + \beta)^2 (\alpha + \beta - 1)}.\]

In Bayesian inference, the beta distribution is the conjugate prior probability distribution for parameters of the Bernoulli, binomial, negative binomial and geometric distribution. The beta distribution is a suitable model for the random behaviour of percentages and proportions.

class cprior.cdist.BetaModel(name='', alpha=1, beta=1)

Bases: cprior.cdist.base.BayesModel

Beta conjugate prior distribution model.

Parameters:
  • alpha (int or float (default=1)) – Prior parameter alpha.
  • beta (int or float (default=1)) – Prior parameter beta.
alpha_posterior

Posterior parameter alpha.

Returns:alpha
Return type:float
beta_posterior

Posterior parameter beta.

Returns:beta
Return type:float
cdf(x)

Cumulative distribution function of the posterior distribution.

Parameters:x (array-like) – Quantiles.
Returns:cdf – Cumulative distribution function evaluated at x.
Return type:numpy.ndarray
credible_interval(interval_length)

Credible interval of the posterior distribution.

Parameters:interval_length (float (default=0.9)) – Compute interval_length% credible interval. This is a value in [0, 1].
Returns:interval – Lower and upper credible interval limits.
Return type:tuple
mean()

Mean of the posterior distribution.

Returns:mean
Return type:float
pdf(x)

Probability density function of the posterior distribution.

Parameters:x (array-like) – Quantiles.
Returns:pdf – Probability density function evaluated at x.
Return type:numpy.ndarray
ppf(q)

Percent point function (quantile) of the posterior distribution.

Parameters:x (array-like) – Lower tail probability.
Returns:ppf – Quantile corresponding to the lower tail probability q.
Return type:numpy.ndarray
rvs(size=1, random_state=None)

Random variates of the posterior distribution.

Parameters:
  • size (int (default=1)) – Number of random variates.
  • random_state (int or None (default=None)) – The seed used by the random number generator.
Returns:

rvs – Random variates of given size.
Return type:

numpy.ndarray or scalar
std()

Standard deviation of the posterior distribution.

Returns:std
Return type:float
var()

Variance of the posterior distribution.

Returns:var
Return type:float
class cprior.cdist.BetaABTest(modelA, modelB, simulations=None, random_state=None)

Bases: cprior.cdist.base.BayesABTest

Bayesian A/B testing with prior beta distribution.

Parameters:
  • modelA (object) – The beta model for variant A.
  • modelB (object) – The beta model for variant B.
  • simulations (int or None (default=1000000)) – Number of Monte Carlo simulations.
  • random_state (int or None (default=None)) – The seed used by the random number generator.
expected_loss(method='exact', variant='A', lift=0, mlhs_samples=10000)

Compute the expected loss. This is the expected uplift lost by choosing a given variant.

  • If variant == "A", \(\mathrm{E}[\max(B - A - lift, 0)]\)
  • If variant == "B", \(\mathrm{E}[\max(A - B - lift, 0)]\)
  • If variant == "all", both.

If lift is positive value, the computation method must be Monte Carlo sampling.

Parameters:
  • method (str (default="exact")) – The method of computation. Options are “exact”, “MC” (Monte Carlo) and “MLHS” (Monte Carlo + Median Latin Hypercube Sampling).
  • variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”.
  • lift (float (default=0.0)) – The amount of uplift.
  • mlhs_samples (int (default=10000)) – Number of samples for MLHS method.
Returns:

expected_loss
Return type:

float or tuple of floats
expected_loss_ci(method='MC', variant='A', interval_length=0.9, ci_method='ETI')

Compute credible intervals on the difference distribution of \(Z = B-A\) and/or \(Z = A-B\).

  • If variant == "A", \(Z = B - A\)
  • If variant == "B", \(Z = A - B\)
  • If variant == "all", both.
Parameters:
  • method (str (default="MC")) – The method of computation. Options are “asymptotic” and “MC”.
  • variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”.
  • interval_length (float (default=0.9)) – Compute interval_length % credible interval. This is a value in [0, 1].
  • ci_method (str (default="ETI")) – Method to compute credible intervals. Supported methods are Highest Density interval (method="HDI) and Equal-tailed interval (method="ETI"). Currently, method="HDI is only available for method="MC".
Returns:

expected_loss_ci
Return type:

np.ndarray or tuple of np.ndarray
expected_loss_relative(method='exact', variant='A')

Compute expected relative loss for choosing a variant. This can be seen as the negative expected relative improvement or uplift.

  • If variant == "A", \(\mathrm{E}[(B - A) / A]\)
  • If variant == "B", \(\mathrm{E}[(A - B) / B]\)
  • If variant == "all", both.
Parameters:
  • method (str (default="exact")) – The method of computation. Options are “exact” and “MC”.
  • variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”.
Returns:

expected_loss_relative
Return type:

float or tuple of floats
expected_loss_relative_ci(method='MC', variant='A', interval_length=0.9, ci_method='ETI')

Compute credible intervals on the relative difference distribution of \(Z = (B-A)/A\) and/or \(Z = (A-B)/B\).

  • If variant == "A", \(Z = (B-A)/A\)
  • If variant == "B", \(Z = (A-B)/B\)
  • If variant == "all", both.
Parameters:
  • method (str (default="MC")) – The method of computation. Options are “asymptotic”, “exact” and “MC”.
  • variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”.
  • interval_length (float (default=0.9)) – Compute interval_length% credible interval. This is a value in [0, 1].
  • ci_method (str (default="ETI")) – Method to compute credible intervals. Supported methods are Highest Density interval (method="HDI) and Equal-tailed interval (method="ETI"). Currently, method="HDI is only available for method="MC".
Returns:

expected_loss_relative_ci
Return type:

np.ndarray or tuple of np.ndarray
probability(method='exact', variant='A', lift=0, mlhs_samples=10000)

Compute the error probability or chance to beat control.

  • If variant == "A", \(P[A > B + lift]\)
  • If variant == "B", \(P[B > A + lift]\)
  • If variant == "all", both.

If lift is positive value, the computation method must be Monte Carlo sampling.

Parameters:
  • method (str (default="exact")) – The method of computation. Options are “exact”, “MC” (Monte Carlo) and “MLHS” (Monte Carlo + Median Latin Hypercube Sampling).
  • variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”.
  • lift (float (default=0.0)) – The amount of uplift.
  • mlhs_samples (int (default=10000)) – Number of samples for MLHS method.
Returns:

probability
Return type:

float or tuple of floats
update_A(data)

Update posterior parameters for variant A with new data samples.

Parameters:data (array-like, shape = (n_samples)) –
update_B(data)

Update posterior parameters for variant B with new data samples.

Parameters:data (array-like, shape = (n_samples)) –
class cprior.cdist.BetaMVTest(models, simulations=None, random_state=None, n_jobs=None)

Bases: cprior.cdist.base.BayesMVTest

Bayesian Multivariate testing with prior beta distribution.

Parameters:
  • models (object) – The beta models.
  • simulations (int or None (default=1000000)) – Number of Monte Carlo simulations.
  • random_state (int or None (default=None)) – The seed used by the random number generator.
expected_loss(method='exact', control='A', variant='B', lift=0, mlhs_samples=10000)

Compute the expected loss. This is the expected uplift lost by choosing a given variant, i.e., \(\mathrm{E}[\max(control - variant - lift, 0)]\).

If lift is positive value, the computation method must be Monte Carlo sampling.

Parameters:
  • method (str (default="exact")) – The method of computation. Options are “exact”, “MC” (Monte Carlo) and “MLHS” (Monte Carlo + Median Latin Hypercube Sampling).
  • control (str (default="A")) – The control variant.
  • variant (str (default="B")) – The tested variant.
  • lift (float (default=0.0)) – The amount of uplift.
  • mlhs_samples (int (default=10000)) – Number of samples for MLHS method.
Returns:

expected_loss
Return type:

float
expected_loss_ci(method='MC', control='A', variant='B', interval_length=0.9, ci_method='ETI')

Compute credible intervals on the difference distribution of \(Z = control-variant\).

Parameters:
  • method (str (default="MC")) – The method of computation. Options are “asymptotic” and “MC”.
  • control (str (default="A")) – The control variant.
  • variant (str (default="B")) – The tested variant.
  • interval_length (float (default=0.9)) – Compute interval_length% credible interval. This is a value in [0, 1].
  • ci_method (str (default="ETI")) – Method to compute credible intervals. Supported methods are Highest Density interval (method="HDI) and Equal-tailed interval (method="ETI"). Currently, method="HDI is only available for method="MC".
Returns:

expected_loss_ci
Return type:

np.ndarray or tuple of np.ndarray
expected_loss_relative(method='exact', control='A', variant='B')

Compute expected relative loss for choosing a variant. This can be seen as the negative expected relative improvement or uplift, i.e., \(\mathrm{E}[(control - variant) / variant]\).

Parameters:
  • method (str (default="exact")) – The method of computation. Options are “exact” and “MC”.
  • control (str (default="A")) – The control variant.
  • variant (str (default="B")) – The tested variant.
Returns:

expected_loss_relative
Return type:

float
expected_loss_relative_ci(method='MC', control='A', variant='B', interval_length=0.9, ci_method='ETI')

Compute credible intervals on the relative difference distribution of \(Z = (control - variant) / variant\).

Parameters:
  • method (str (default="MC")) – The method of computation. Options are “asymptotic”, “exact” and “MC”.
  • control (str (default="A")) – The control variant.
  • variant (str (default="B")) – The tested variant.
  • interval_length (float (default=0.9)) – Compute interval_length% credible interval. This is a value in [0, 1].
  • ci_method (str (default="ETI")) – Method to compute credible intervals. Supported methods are Highest Density interval (method="HDI) and Equal-tailed interval (method="ETI"). Currently, method="HDI is only available for method="MC".
Returns:

expected_loss_relative_ci
Return type:

np.ndarray or tuple of np.ndarray
expected_loss_relative_vs_all(method='quad', control='A', variant='B', mlhs_samples=1000)

Compute the expected relative loss against all variations. For example, given variants “A”, “B”, “C” and “D”, and choosing variant=”B”, we compute \(\mathrm{E}[(\max(A, C, D) - B) / B]\).

Parameters:
  • method (str (default="quad")) – The method of computation. Options are “MC” (Monte Carlo), “MLHS” (Monte Carlo + Median Latin Hypercube Sampling) and “quad” (numerical integration).
  • variant (str (default="B")) – The chosen variant.
  • mlhs_samples (int (default=1000)) – Number of samples for MLHS method.
Returns:

expected_loss_relative_vs_all
Return type:

float
expected_loss_vs_all(method='quad', variant='B', lift=0, mlhs_samples=1000)

Compute the expected loss against all variations. For example, given variants “A”, “B”, “C” and “D”, and choosing variant=”B”, we compute \(\mathrm{E}[\max(\max(A, C, D) - B, 0)]\).

If lift is positive value, the computation method must be Monte Carlo sampling.

Parameters:
  • method (str (default="quad")) – The method of computation. Options are “MC” (Monte Carlo), “MLHS” (Monte Carlo + Median Latin Hypercube Sampling) and “quad” (numerical integration).
  • variant (str (default="B")) – The chosen variant.
  • lift (float (default=0.0)) – The amount of uplift.
  • mlhs_samples (int (default=1000)) – Number of samples for MLHS method.
Returns:

expected_loss_vs_all
Return type:

float
probability(method='exact', control='A', variant='B', lift=0, mlhs_samples=10000)

Compute the error probability or chance to beat control, i.e., \(P[variant > control + lift]\).

If lift is positive value, the computation method must be Monte Carlo sampling.

Parameters:
  • method (str (default="exact")) – The method of computation. Options are “exact”, “MC” (Monte Carlo) and “MLHS” (Monte Carlo + Median Latin Hypercube Sampling).
  • control (str (default="A")) – The control variant.
  • variant (str (default="B")) – The tested variant.
  • lift (float (default=0.0)) – The amount of uplift.
  • mlhs_samples (int (default=10000)) – Number of samples for MLHS method.
Returns:

probability
Return type:

float
probability_vs_all(method='quad', variant='B', lift=0, mlhs_samples=1000)

Compute the error probability or chance to beat all variations. For example, given variants “A”, “B”, “C” and “D”, and choosing variant=”B”, we compute \(P[B > \max(A, C, D) + lift]\).

If lift is positive value, the computation method must be Monte Carlo sampling.

Parameters:
  • method (str (default="quad")) – The method of computation. Options are “MC” (Monte Carlo), “MLHS” (Monte Carlo + Median Latin Hypercube Sampling) and “quad” (numerical integration).
  • variant (str (default="B")) – The chosen variant.
  • lift (float (default=0.0)) – The amount of uplift.
  • mlhs_samples (int (default=1000)) – Number of samples for MLHS method.
Returns:

probability_vs_all
Return type:

float
update(data, variant)

Update posterior parameters for a given variant with new data samples.

Parameters:
  • data (array-like, shape = (n_samples)) –
  • variant (str) –