bacontrees: An extensible framework for Bayesian Context Trees in R
Abstract
Context tree models, or variable-length Markov chains, describe symbolic sequences whose dependence structure varies with context length. Bayesian formulations of these models enable uncertainty quantification and regularization through priors on tree structures and transition probabilities. Despite their theoretical appeal, practical implementations have been fragmented and model-specific. The bacontrees package for R provides a unified framework for Bayesian Context Trees by defining core data structures and a consistent interface for building, modifying and manipulating trees. Rather than implementing a single inference method, bacontrees offers the computational foundation upon which diverse Bayesian and frequentist algorithms can be developed. This separation of representation and inference promotes reproducibility and facilitates experimentation with new modeling strategies. The package aims to support applications in information theory, linguistics, and other domains where context-based probabilistic modeling is central.
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BibTeX
@misc{freguglia2026bacontrees,
title = {{bacontrees: An extensible framework for Bayesian Context Trees in R}},
author = {Victor Freguglia and Thiago Paulichen},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.03408},
eprint = {2611.03408},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.03408},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - bacontrees: An extensible framework for Bayesian Context Trees in R
AU - Victor Freguglia
AU - Thiago Paulichen
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.03408
UR - https://arxiv.org/abs/2611.03408
AB - Context tree models, or variable-length Markov chains, describe symbolic sequences whose dependence structure varies with context length. Bayesian formulations of these models enable uncertainty quantification and regularization through priors on tree structures and transition probabilities. Despite their theoretical appeal, practical implementations have been fragmented and model-specific. The bacontrees package for R provides a unified framework for Bayesian Context Trees by defining core data structures and a consistent interface for building, modifying and manipulating trees. Rather than implementing a single inference method, bacontrees offers the computational foundation upon which diverse Bayesian and frequentist algorithms can be developed. This separation of representation and inference promotes reproducibility and facilitates experimentation with new modeling strategies. The package aims to support applications in information theory, linguistics, and other domains where context-based probabilistic modeling is central.
ER -