HOMC: A MATLAB Package for Higher Order Markov Chains

We present a MATLAB package, which is the first of its kind, for Higher Order Markov Chains (HOMC). It can be used to easily compute all important quantities in our recent works relevant to higher order Markov chains, such as the \(k\) -step transition tensor, limiting probability distribution, ever-reaching probability tensor, and mean first passage time tensor. It can also be used to check whether a higher order chain is ergodic or regular, to construct the transition matrix of the associated reduced first order chain, and to determine whether a state is recurrent or transient. A key function in the package is an implementation of the tensor “box” product which has a probabilistic interpretation and is different from other tensor products in the literature. This HOMC package is useful to researchers and practitioners alike for tasks such as numerical experimentation and algorithm prototyping involving higher order Markov chains.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Mathematical Software
Published
2026-07-27
DOI
https://doi.org/10.1145/3834564
Citations
1
Primary Topic
Tensor decomposition and applications
Type
article
Field-Weighted Citation Impact
8.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

HOMC: A MATLAB Package for Higher Order Markov Chains

XU Jian-hong
1 citations
ACM Transactions on Mathematical Software
Tensor decomposition and applications
8.26
article

HOMC: A MATLAB Package for Higher Order Markov Chains

XU Jian-hong
article en
1 citations

Abstract

We present a MATLAB package, which is the first of its kind, for Higher Order Markov Chains (HOMC). It can be used to easily compute all important quantities in our recent works relevant to higher order Markov chains, such as the \(k\) -step transition tensor, limiting probability distribution, ever-reaching probability tensor, and mean first passage time tensor. It can also be used to check whether a higher order chain is ergodic or regular, to construct the transition matrix of the associated reduced first order chain, and to determine whether a state is recurrent or transient. A key function in the package is an implementation of the tensor “box” product which has a probabilistic interpretation and is different from other tensor products in the literature. This HOMC package is useful to researchers and practitioners alike for tasks such as numerical experimentation and algorithm prototyping involving higher order Markov chains.

ACM Transactions on Mathematical Software
Southern Illinois University Carbondale (US)
Openalex Percentile: Top 8%
Tensor decomposition and applications
8.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

HOMC: A MATLAB Package for Higher Order Markov Chains — XU Jian-hong · ACM Transactions on Mathematical Software (2026) | TGRS Research Map | TGRS