# Bayesian Markov Switching Tensor Regression for Time-varying Networks

arXiv: https://arxiv.org/abs/1711.00097 · HTML: https://mlsanity.com/paper/1711.00097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Under-Counted Tensor Completion with Neural Incorporation of Attributes](https://mlsanity.com/paper/2306.03273/) (2023)
- [Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework](https://mlsanity.com/paper/2312.16707/) (2023)

## Abstract (arXiv)

We propose a new Bayesian Markov switching regression model for multidimensional arrays (tensors) of binary time series. We assume a zero-inflated logit regression with time-varying parameters and apply it to multilayer temporal networks. The original contribution is threefold. First, to avoid over-fitting we propose a parsimonious parametrization based on a low-rank decomposition of the tensor of regression coefficients. Second, we assume the parameters are driven by a hidden Markov chain, thus allowing for structural changes in the network topology. We follow a Bayesian approach to inference and provide an efficient Gibbs sampler for posterior approximation. We apply the methodology to a real dataset of financial networks to study the impact of several risk factors on the edge probability. Supplementary materials for this article are available online.