expct

Compute the expectation of some query variable(s), optionally conditioned
on some event(s).

Adversarial random forests (ARFs) recursively partition data into
fully factorized leaves, where features are jointly independent. The
procedure is iterative, with alternating rounds of generation and
discrimination. Data becomes increasingly realistic at each round, until
original and synthetic samples can no longer be reliably distinguished.
This is useful for several unsupervised learning tasks, such as density
estimation and data synthesis. Methods for both are implemented in this
package. ARFs naturally handle unstructured data with mixed continuous and
categorical covariates. They inherit many of the benefits of random forests,
including speed, flexibility, and solid performance with default parameters.
For details, see Watson et al. (2022) <arXiv:2205.09435>.

Marvin Wright

Adversarial Random Forests

Marvin N. Wright

David S. Watson

Kristin Blesch

Jan Kapar

expct function

<dl><dt>params</dt>
<dd>Circuit parameters learned via <code>forde</code>.</dd>
<dt>query</dt>
<dd>Optional character vector of variable names. Estimates will be
computed for each. If <code>NULL</code>, all variables other than those in
<code>evidence</code> will be estimated.</dd>
<dt>evidence</dt>
<dd>Optional set of conditioning events. This can take one of
three forms: (1) a partial sample, i.e. a single row of data with some but
not all columns; (2) a data frame of conditioning events, which allows for
inequalities; or (3) a posterior distribution over leaves. See Details.</dd></dl>

Arguments

Expected Value — expct

<dl>

<dt>params</dt>
<dd>Circuit parameters learned via <code>forde</code>.</dd>


<dt>query</dt>
<dd>Optional character vector of variable names. Estimates will be
computed for each. If <code>NULL</code>, all variables other than those in
<code>evidence</code> will be estimated.</dd>


<dt>evidence</dt>
<dd>Optional set of conditioning events. This can take one of
three forms: (1) a partial sample, i.e. a single row of data with some but
not all columns; (2) a data frame of conditioning events, which allows for
inequalities; or (3) a posterior distribution over leaves. See Details.</dd>

</dl>

expct: Expected Value

Description

Usage

Value

Arguments

Details

References

See Also

Examples