Citing sequince

Citing sequince#

Software citations#

If you use sequince, please cite the Zenodo archive:

Williams, M. J., and Chapman-Bird, C. (2026). sequince-dev/sequince. Zenodo. doi:10.5281/zenodo.23596103.

If you also use sequince-density, please cite its Zenodo archive:

Williams, M. J. (2026). sequince-dev/sequince-density. Zenodo. doi:10.5281/zenodo.23687687.

Ideally, cite the Zenodo record for the exact version of each package used in your analysis. Select that version on Zenodo and export its citation in your preferred format.

Algorithm references#

Please also cite the methods used in your analysis. These references credit algorithm development; they are separate from the software citation.

  • SMC samplers: Del Moral, P., Doucet, A., and Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436. doi:10.1111/j.1467-9868.2006.00553.x.

  • Persistent sampling: Karamanis, M., and Seljak, U. (2025). Persistent sampling: enhancing the efficiency of sequential Monte Carlo. Statistics and Computing, 35, 144. doi:10.1007/s11222-025-10682-y.

  • Waste-free SMC: Dau, H.-D., and Chopin, N. (2022). Waste-free sequential Monte Carlo. Journal of the Royal Statistical Society: Series B, 84(1), 114–148. doi:10.1111/rssb.12475.

  • Nested sampling via SMC (NS-SMC): Salomone, R., South, L. F., Drovandi, C., Kroese, D. P., and Johansen, A. M. (2025). Unbiased and consistent nested sampling via sequential Monte Carlo. Journal of the Royal Statistical Society Series B: Statistical Methodology, 87(4), 1221–1238. doi:10.1093/jrsssb/qkaf015.

  • Flow-preconditioned SMC: Karamanis, M., Beutler, F., Peacock, J. A., Nabergoj, D., and Seljak, U. (2022). Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo. doi:10.1093/mnras/stac2272.

For mutation backends, density estimators, and flow architectures, also follow the citation guidance of the packages and methods used.