# Bayesian Methods in Tensor Analysis

arXiv: https://arxiv.org/abs/2302.05978 · HTML: https://mlsanity.com/paper/2302.05978/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2023.

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

- [Efficient Decision Trees for Tensor Regressions](https://mlsanity.com/paper/2408.01926/) (2024)
- [Geological Field Restoration through the Lens of Image Inpainting](https://mlsanity.com/paper/2506.04869/) (2025)

## Abstract (arXiv)

Tensors, also known as multidimensional arrays, are useful data structures in machine learning and statistics. In recent years, Bayesian methods have emerged as a popular direction for analyzing tensor-valued data since they provide a convenient way to introduce sparsity into the model and conduct uncertainty quantification. In this article, we provide an overview of frequentist and Bayesian methods for solving tensor completion and regression problems, with a focus on Bayesian methods. We review common Bayesian tensor approaches including model formulation, prior assignment, posterior computation, and theoretical properties. We also discuss potential future directions in this field.