tags

Preference Learning

Dec 2024

Sequential Rank and Preference Learning with the Bayesian Mallows Model

This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. Published in Bayesian Analysis.

  • Mallows mixtures
  • partial rankings
  • particle filter
  • preference learning
  • SMC$^{2}$