# Parameter identification in Markov chain choice models

arXiv: https://arxiv.org/abs/1706.00729 · HTML: https://mlsanity.com/paper/1706.00729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Choice Modeling](https://mlsanity.com/paper/2208.09325/) (2022)
- [A Neural Network Based Choice Model for Assortment Optimization](https://mlsanity.com/paper/2308.05617/) (2023)
- [Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data](https://mlsanity.com/paper/2607.09817/) (2026)

## Abstract (arXiv)

This work studies the parameter identification problem for the Markov chain choice model of Blanchet, Gallego, and Goyal used in assortment planning. In this model, the product selected by a customer is determined by a Markov chain over the products, where the products in the offered assortment are absorbing states. The underlying parameters of the model were previously shown to be identifiable from the choice probabilities for the all-products assortment, together with choice probabilities for assortments of all-but-one products. Obtaining and estimating choice probabilities for such large assortments is not desirable in many settings. The main result of this work is that the parameters may be identified from assortments of sizes two and three, regardless of the total number of products. The result is obtained via a simple and efficient parameter recovery algorithm.