Distributions¶

The distributions module provides probability distributions used for shocks and initial conditions. Continuous distributions support both drawing random samples and discretization into point-mass approximations.

Note

Normal and Lognormal are parameterized by the distribution’s mean and standard deviation in levels, not by the log-space parameters \((\mu, \sigma)\) familiar from other libraries. A standard deviation of zero produces a degenerate point mass at the mean argument.

Distribution classes that provide compatibility with scipy.stats and torch.distributions for better integration with neural network methods while maintaining API compatibility.

class skagent.distributions.Bernoulli(p=0.5, backend='scipy', rng=None)¶

Bernoulli distribution compatible with skagent.distributions.Bernoulli

Parameters:

p (float)
backend (str)
rng (Generator | None)

discretize(**kwargs)¶

Bernoulli is already discrete

Return type:: DiscreteDistribution

property mean: float¶: Mean of the distribution

property std: float¶: Standard deviation of the distribution

class skagent.distributions.DiscreteDistribution(points, weights, var_names=None, rng=None)¶

A discrete distribution representation for labeled discrete distributions

Parameters:

points (ndarray)
weights (ndarray)
var_names (list[str] | None)
rng (Generator | None)

draw(n=1)¶

Draw samples from the discrete distribution

Parameters:: n (int)
Return type:: ndarray

class skagent.distributions.DiscreteDistributionLabeled(points, weights, var_names=None, rng=None)¶

Labeled discrete distribution with variable names

Parameters:

points (ndarray)
weights (ndarray)
var_names (list[str] | None)
rng (Generator | None)

classmethod from_unlabeled(unlabeled_dist, var_names)¶

Create labeled distribution from unlabeled one

Parameters:: var_names (list[str])

class skagent.distributions.Distribution(backend='scipy', rng=None)¶

Base class for all distributions, providing a common interface that works with both scipy and torch backends.

Parameters:

backend (str)
rng (Generator | None)

abstractmethod discretize(**kwargs)¶

Discretize the distribution

Return type:: DiscreteDistribution

draw(n=1)¶

Draw n samples from the distribution

Parameters:: n (int)
Return type:: ndarray

abstract property mean: float¶: Mean of the distribution

abstract property std: float¶: Standard deviation of the distribution

class skagent.distributions.IndexDistribution(dist_class, params_dict, rng=None)¶

Distribution that varies by index (like age), compatible with skagent.distributions.IndexDistribution

Parameters:

params_dict (dict[str, list])
rng (Generator | None)

draw(conditions)¶

Draw samples based on conditions (typically ages)

Parameters:: conditions (ndarray)
Return type:: ndarray

class skagent.distributions.Lognormal(mean=1.0, std=1.0, backend='scipy', rng=None)¶

Lognormal distribution compatible with skagent.distributions.Lognormal

Parameters:

mean (float)
std (float)
backend (str)
rng (Generator | None)

discretize(n_points=7, N=None, **kwargs)¶

Discretize the distribution

Parameters:

n_points (int)
N (int | None)

Return type:

DiscreteDistribution

draw(n=1)¶

Draw n samples from the distribution

Parameters:: n (int)
Return type:: ndarray

property mean: float¶: Mean of the distribution

property std: float¶: Standard deviation of the distribution

class skagent.distributions.MeanOneLogNormal(sigma=1.0, backend='scipy', rng=None)¶

Lognormal distribution with mean normalized to 1.0

Parameters:

sigma (float)
backend (str)
rng (Generator | None)

class skagent.distributions.Normal(mu=0.0, sigma=1.0, backend='scipy', rng=None)¶

Normal distribution compatible with skagent.distributions.Normal

Parameters:

mu (float)
sigma (float)
backend (str)
rng (Generator | None)

discretize(n_points=7, sigma_range=3.0, N=None, **kwargs)¶

Discretize using Gauss-Hermite quadrature or uniform grid

Parameters:

n_points (int)
sigma_range (float)
N (int | None)

Return type:

DiscreteDistribution

property mean: float¶: Mean of the distribution

property std: float¶: Standard deviation of the distribution

class skagent.distributions.TimeVaryingDiscreteDistribution(distributions)¶

Time-varying discrete distribution for compatibility

Parameters:: distributions (list[DiscreteDistribution])

draw(conditions)¶

Draw samples based on time conditions

Parameters:: conditions (ndarray)
Return type:: ndarray

class skagent.distributions.Uniform(low=0.0, high=1.0, backend='scipy', rng=None)¶

Uniform distribution

Parameters:

low (float)
high (float)
backend (str)
rng (Generator | None)

discretize(n_points=7, N=None, **kwargs)¶

Discretize using Gauss-Hermite quadrature or uniform grid

Parameters:

n_points (int)
N (int | None)

Return type:

DiscreteDistribution

property mean: float¶: Mean of the distribution

property std: float¶: Standard deviation of the distribution

skagent.distributions.combine_indep_dstns(*distributions)¶

Combine independent discrete distributions into a joint distribution Compatible with skagent.distributions.combine_indep_dstns

Return type:: DiscreteDistribution

skagent.distributions.expected(func, dist)¶

Compute expected value of a function over a discrete distribution Compatible with skagent.distributions.expected

Parameters:: dist (DiscreteDistribution)
Return type:: float