# Uniform distribution¶

The uniform distribution is a continuous distribution with constant probability in its support defined by the two parameters, $$a$$ and $$b$$, which are its minimum and maximum values. The probability density function for $$x \in [a, b]$$ is given by

$f(x; a, b) = \frac{1}{b-a},$

and $$0$$ elsewhere. The cumulative distribution is

$\begin{split}F(x; a, b) = \begin{cases} 0, & x < a\\ \frac{x-a}{b-a}, & x \in [a, b)\\ 1, & x \ge b \end{cases}\end{split}$

The expected value and variance are as follows

$\mathrm{E}[X] = \frac{a + b}{2}, \quad \mathrm{Var}[X] = \frac{(b-a)^2}{12}.$

The uniform distribution is used to model events that are equally likely.

class cprior.models.UniformModel(name='', scale=0.005, shape=0.005)

Bases: cprior.cdist.pareto.ParetoModel

Bayesian model with uniform likelihood and a Pareto prior distribution.

Given data samples $$\mathbf{x} = (x_1, \ldots, x_n)$$ from a uniform distribution with zero lower boundary and upper boundary $$\theta$$, the posterior distribution is

$\theta | \mathbf{x} \sim \mathcal{PA}(\alpha + n, \max(\beta, x_{max}))$

with prior parameters $$\alpha$$ (shape), $$\beta$$ (scale) and $$x_{max}$$ is the sample maximum.

Parameters: name (str (default="")) – Model name. scale (float (default=0.005)) – Prior parameter scale. shape (float (default=0.005)) – Prior parameter shape.
n_samples_

Number of samples.

Type: int
cdf(x)

Cumulative distribution function of the posterior distribution.

Parameters: x (array-like) – Quantiles. cdf – Cumulative distribution function evaluated at x. 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]. interval – Lower and upper credible interval limits. tuple
mean()

Mean of the posterior distribution.

Returns: mean float
pdf(x)

Probability density function of the posterior distribution.

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

Percent point function (quantile) of the posterior distribution.

Parameters: x (array-like) – Lower tail probability. ppf – Quantile corresponding to the lower tail probability q. numpy.ndarray
ppmean()

Posterior predictive mean.

If $$X$$ follows a uniform distribution with zero lower boundary and upper boundary $$\theta$$, the posterior predictive expected value is given by

$\mathrm{E}[X] = \frac{\alpha \beta}{2(\alpha - 1)},$

where $$\alpha$$ and $$\beta$$ are the posterior values of the parameters.

Returns: mean float
pppdf(x)

Posterior predictive probability density function.

If $$X$$ follows a uniform distribution with zero lower boundary and upper boundary $$\theta$$, the posterior predictive probability density function is given by

$\begin{split}f(x; \alpha, \beta) = \begin{cases} \frac{\alpha}{(\alpha + 1) \beta}, & 0 < x < \beta,\\ \frac{\alpha \beta^{\alpha}}{(\alpha + 1)x^{\alpha + 1}}, & x \ge \beta \end{cases}\end{split}$

where $$\alpha$$ and $$\beta$$ are the posterior values of the parameters.

Parameters: x (array-like) – Quantiles. pdf – Probability density function evaluated at x. float
ppvar()

Posterior predictive variance.

If $$X$$ follows a uniform distribution with zero lower boundary and upper boundary $$\theta$$, the posterior predictive variance is given by

$\mathrm{Var}[X] = \frac{\alpha (\alpha^2 - 2\alpha + 4)\beta^2} {12(\alpha - 1)^2 (\alpha - 2)},$

where $$\alpha$$ and $$\beta$$ are the posterior values of the parameters.

Returns: var float
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. rvs – Random variates of given size. numpy.ndarray or scalar
scale_posterior

Posterior parameter scale.

Returns: scale float
shape_posterior

Posterior parameter shape.

Returns: shape float
std()

Standard deviation of the posterior distribution.

Returns: std float
update(data)

Update posterior parameters with new data.

Parameters: data (array-like, shape = (n_samples)) – Data samples from a uniform distribution.
var()

Variance of the posterior distribution.

Returns: var float
class cprior.models.UniformABTest(modelA, modelB, simulations=1000000, random_state=None)

Bases: cprior.cdist.pareto.ParetoABTest

Uniform A/B test.

Parameters: modelA (object) – The control model. modelB (object) – The variation model. 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)

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” and “MC”. variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”. lift (float (default=0.0)) – The amount of uplift. expected_loss 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. 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". expected_loss_ci 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”. expected_loss_relative 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. 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". expected_loss_relative_ci np.ndarray or tuple of np.ndarray
probability(method='exact', variant='A', lift=0)

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” and “MC”. variant (str (default="A")) – The chosen variant. Options are “A”, “B”, “all”. lift (float (default=0.0)) – The amount of uplift. probability 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.models.UniformMVTest(models, simulations=1000000, random_state=None, n_jobs=None)

Bases: cprior.cdist.pareto.ParetoMVTest

Uniform Multivariate test.

Parameters: models (dict) – The control and variations 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)

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” and “MC”. control (str (default="A")) – The control variant. variant (str (default="B")) – The tested variant. lift (float (default=0.0)) – The amount of uplift. expected_loss 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. 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". expected_loss_ci 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. expected_loss_relative 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. 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". expected_loss_relative_ci 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="MLHS")) – 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. expected_loss_relative_vs_all 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. expected_loss_vs_all float
probability(method='exact', control='A', variant='B', lift=0)

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” and “MC”. control (str (default="A")) – The control variant. variant (str (default="B")) – The tested variant. lift (float (default=0.0)) – The amount of uplift. probability 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="MLHS")) – 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. probability_vs_all float
update(data, variant)

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

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