marginal

Compute the marginal probability, GMM under the Hamming distance, 
of a distance decomposition vector for which some positions are known and some are not

Includes functions to work with the Mallows and Generalized Mallows
Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam
and it includes functions for making inference, sampling and learning such
distributions, some of which are novel in the literature. As a by-product,
PerMallows also includes operations for permutations, paying special attention
to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It
is also possible to generate random permutations at a given distance, or with
a given number of inversions, or cycles, or fixed points or even with a given
length on LIS (longest increasing subsequence).

Ekhine Irurozki

PerMallows

Permutations and Mallows Distributions

Borja Calvo

marginal function

<dl><dt>h</dt>
<dd>n dimensional distance decomposition vector where h_j = 0 means that $j$ is a fixed point, 
h_j = 1 means that $j$ is an unfixed point and otherwise $j$ is not known</dd>
<dt>theta</dt>
<dd>n dimensional distance decomposition vector with the dispersion parameters</dd></dl>

Arguments

Compute the marginal probability, GMM under the Hamming distance — marginal

<dl>

<dt>h</dt>
<dd>n dimensional distance decomposition vector where h_j = 0 means that $j$ is a fixed point, 
h_j = 1 means that $j$ is an unfixed point and otherwise $j$ is not known</dd>


<dt>theta</dt>
<dd>n dimensional distance decomposition vector with the dispersion parameters</dd>

</dl>

marginal: Compute the marginal probability, GMM under the Hamming distance

Description

Usage

Value

Arguments

References

Examples