Scalable Model-Based Clustering with Sequential Monte Carlo

ArXi:2604.14810v1 Announce Type: cross In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until data are observed. This difficulty is compounded when clusters follow complex distributions, as is the case with text data. Sequential Monte Carlo (SMC) methods give a natural way of representing and updating this uncertainty over time, but have prohibitive memory requirements for large-scale problems.