Function reference

Distributions

Core distribution interfaces and implementations.

Distribution	Top-level abstract base class for probability distributions.
Normal	Normal / Gaussian distribution parameterized by mu and sigma.
LogNormal	Lognormal distribution parameterized by underlying normal mu and sigma.
LogitNormal	Logit-normal distribution for values in the unit interval.
Beta	Beta distribution for values in the unit interval.
StretchedBeta	Beta distribution scaled to arbitrary finite bounds.
PERT	PERT distribution as a stretched beta with concentration fixed at 4.
Gamma	Gamma distribution parameterized by shape alpha and rate beta.
Exponential	Exponential distribution parameterized by rate lam.
Bernoulli	Bernoulli distribution for binary outcomes, parameterized by p.
Binomial	Binomial distribution parameterized by trials n and probability p.
Poisson	Poisson distribution parameterized by rate lam.
Geometric	Geometric distribution for the number of failures before first success.
NegativeBinomial	Negative binomial distribution for failures before r successes.
UvMixture	Weighted mixture of one or more univariate component distributions.

Distribution domains

Marker classes for distribution domain semantics.

UvDistribution	Base class for distributions over a single variable.
UvContinuous	Base class for univariate continuous distributions.
UvRealContinuous	Continuous distribution with support over all real numbers.
UvPositiveContinuous	Continuous distribution with positive support.
UvBoundedContinuous	Continuous distribution with finite lower and upper support.
UvUnitBoundedContinuous	Continuous distribution with support on the unit interval.
UvDiscrete	Base class for univariate discrete distributions.
UvFiniteDiscrete	Discrete distribution with finite support.
UvCountDiscrete	Discrete distribution over non-negative integer counts.

Correlation and copulas

Tools for correlated sampling.

CorrelationMatrix	Represent and validate a numeric correlation matrix.
Copula	Base class for copulas that jointly sample marginal distributions.
GaussianCopula	Sample marginal distributions with dependence induced by a Gaussian copula.
StudentTCopula	Sample marginals with dependence induced by a Student-t copula.

Monte Carlo models

Composable model helpers.

MCModel	Lazy Monte Carlo expression composed from distributions, constants, and models.
MCOperation	Serializable operations supported by Monte Carlo model expressions.
where	Build a lazy model expression equivalent to numpy.where.

Sensitivity analysis

One-at-a-time sensitivity tooling.

OneAtATimeSensitivity	Serializable one-at-a-time sensitivity analysis configuration.
one_at_a_time	Run one-at-a-time sensitivity analysis for a target.

Decision trees

Decision tree primitives.

DTree	A sampleable, serializable decision tree.
DTreeNode	Abstract base class for decision tree nodes.
DecisionNode	Decision node that selects the branch with the highest expected value.
ChanceNode	Chance node that follows branches according to their probabilities.
OutcomeNode	Terminal node containing a scalar, distribution, or Monte Carlo model value.
DecisionBranch	A branch leaving a decision node.
ChanceBranch	A probability-weighted branch leaving a chance node.