probabilistic_model.distributions.uniform

probabilistic_model.distributions.uniform#

Classes#

UniformDistribution

Class for uniform distributions over the half-open interval [lower, upper).

Module Contents#

class probabilistic_model.distributions.uniform.UniformDistribution(variable: probabilistic_model.distributions.distributions.Continuous, interval: probabilistic_model.distributions.distributions.SimpleInterval)#

Bases: probabilistic_model.distributions.distributions.ContinuousDistributionWithFiniteSupport

Class for uniform distributions over the half-open interval [lower, upper).

variable#
interval#

The interval of the distribution.

log_likelihood_without_bounds_check(x: probabilistic_model.distributions.distributions.np.array) → probabilistic_model.distributions.distributions.np.array#

Evaluate the logarithmic likelihood function at x without checking the inclusion into the interval. :param x: x where p(x) > 0 :return: log(p(x))

cdf(x: probabilistic_model.distributions.distributions.np.array) → probabilistic_model.distributions.distributions.np.array#

Calculate the cumulative distribution function at x. :param x: The data :return: The cumulative distribution function at x

univariate_log_mode() → probabilistic_model.distributions.distributions.Tuple[probabilistic_model.distributions.distributions.AbstractCompositeSet, float]#
Returns:

The univariate mode of the distribution and its log-likelihood. The mode is not an Event.

log_conditional_from_simple_interval(interval: probabilistic_model.distributions.distributions.SimpleInterval) → probabilistic_model.distributions.distributions.Tuple[probabilistic_model.distributions.distributions.Self, float]#

Calculate the truncated distribution given a simple interval.

Parameters:

interval – The simple interval

Returns:

The truncated distribution and the log-probability of the interval.

sample(amount: int) → probabilistic_model.distributions.distributions.np.array#

Sample from the model.

Parameters:

amount – The number of samples to draw.

Returns:

The samples.

pdf_value() → float#

Calculate the density of the uniform distribution.

log_pdf_value() → float#

Calculate the log-density of the uniform distribution.

moment(order: probabilistic_model.distributions.distributions.OrderType, center: probabilistic_model.distributions.distributions.CenterType) → probabilistic_model.distributions.distributions.MomentType#

Calculate the (centralized) moment of the distribution.

\[\int_{-\infty}^{\infty} (x - center)^{order} pdf(x) dx\]

Note

You can read more about queries of this class in Definition 22 in :cite:p:`choi2020probabilistic`_. [US20]

Parameters:

  • order – The orders of the moment as a variable map for every continuous and integer variable.

  • center – The center of the moment as a variable map for every continuous and integer variable.

Returns:

The moments of the variables in order.

__eq__(other)#
property drawio_label#

The label of the object as a drawio compatible string.

property representation#

The symbol used to represent this distribution.

property abbreviated_symbol: str#
__repr__()#
property image#
__copy__()#
__deepcopy__(memo=None)#
to_json() → probabilistic_model.distributions.distributions.Dict[str, probabilistic_model.distributions.distributions.Any]#
classmethod _from_json(data: probabilistic_model.distributions.distributions.Dict[str, probabilistic_model.distributions.distributions.Any]) → probabilistic_model.distributions.distributions.Self#

Create a variable from a json dict. This method is called from the from_json method after the correct subclass is determined and should be overwritten by the respective subclass.

Parameters:

data – The json dict

Returns:

The deserialized object

x_axis_points_for_plotly() → probabilistic_model.distributions.distributions.List[probabilistic_model.distributions.distributions.Union[None, float]]#
pdf_trace() → probabilistic_model.distributions.distributions.go.Scatter#
cdf_trace() → probabilistic_model.distributions.distributions.go.Scatter#
plot(**kwargs) → probabilistic_model.distributions.distributions.List#

Generate traces that can be plotted with plotly.

Parameters:

  • number_of_samples – The number of samples to draw.

  • surface – If True, plot the model as a surface plot.

  • mode – If True, plot the mode of the model.

Returns:

The traces.

__hash__()#
Contents