PreprintarXiv licencearXiv
Inference in an interactive multi-type branching network
Abstract
We study statistical inference for a high-dimensional stochastic interaction model observed only through low-dimensional aggregate data. The model describes pairwise communication between d individuals as a collection of mutually interacting branching processes with immigration, where activity on one directed pair may trigger subsequent activity in the reverse direction. The latent interaction counts are not observed. Instead, for each individual, only the total number of messages sent and received in each time slot is available. This aggregation gives rise to an inverse problem in which the parameters governing the latent pairwise dynamics must be recovered from substantially compressed observations.
Related
- Multi-type branching diffusions with small mutation rates
- Branching Random Walks on Multi-Type Galton–Watson Trees: A Multifractal Analysis
- Minimax and adaptive inference for branching ratios under long memory
- Convergence of a drainage network model with branching to the Brownian net
- A multitype Markovian branching process with one-type population size dependence
BibTeX
@misc{hasanli2026inference,
title = {{Inference in an interactive multi-type branching network}},
author = {Gunay Hasanli and Michel Mandjes and Jiesen Wang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00152},
eprint = {2611.00152},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00152},
}
RIS
TY - UNPB
TI - Inference in an interactive multi-type branching network
AU - Gunay Hasanli
AU - Michel Mandjes
AU - Jiesen Wang
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00152
UR - https://arxiv.org/abs/2611.00152
AB - We study statistical inference for a high-dimensional stochastic interaction model observed only through low-dimensional aggregate data. The model describes pairwise communication between d individuals as a collection of mutually interacting branching processes with immigration, where activity on one directed pair may trigger subsequent activity in the reverse direction. The latent interaction counts are not observed. Instead, for each individual, only the total number of messages sent and received in each time slot is available. This aggregation gives rise to an inverse problem in which the parameters governing the latent pairwise dynamics must be recovered from substantially compressed observations.
ER -