realized_dist

A <code>realized_dist</code> is an <code>empirical_dist</code> that preserves
provenance: it remembers the source distribution that generated its
samples. All <code>empirical_dist</code> methods work unchanged via S3
inheritance.

internal

Provides an algebra over probability distributions enabling
composition, sampling, and automatic simplification to closed forms.
Supports normal, exponential, gamma, Weibull, chi-squared, uniform,
beta, log-normal, Poisson, multivariate normal, empirical, and mixture
distributions with algebraic operators (addition, subtraction,
multiplication, division, power, exp, log, min, max) that automatically
simplify when mathematical identities apply. Includes closed-form MVN
conditioning (Schur complement), affine transformations, mixture
marginals/conditionals (Bayes rule), and limiting distribution builders
(CLT, LLN, delta method). Uses S3 classes for distributions and R6
for support objects.

Alexander Towell

algebraic.dist

Algebra over Probability Distributions

realized_dist function

<dl><dt>data</dt>
<dd>A matrix of sampled data (rows = observations).</dd>
<dt>source</dt>
<dd>The distribution object that generated <code>data</code>.</dd>
<dt>n</dt>
<dd>The number of samples drawn.</dd></dl>

Arguments

Construct a realized distribution object. — realized_dist

<dl>

<dt>data</dt>
<dd>A matrix of sampled data (rows = observations).</dd>


<dt>source</dt>
<dd>The distribution object that generated <code>data</code>.</dd>


<dt>n</dt>
<dd>The number of samples drawn.</dd>

</dl>

realized_dist: Construct a realized distribution object.

Description

Usage

Value

Arguments