Journal articles
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states. Ou, R., Young, A.L., Sen, D., & Dunson, D. (2024). [arXiv]
Bayesian Analysis [Journal]
Clustering Multiple Sclerosis Medication Sequence Data with Mixture Markov Chain Analysis with covariates using Multiple Simplex Constrained Optimization Routine (MSiCOR). Das, P., Sen, D., De, D., Hou, J., Abar, Z. S. H., Kim, N., Xia, Z., & Cai, T. (2023). [arxiv]
Journal of Computational and Graphical Statistics [Journal]
Posterior computation with the Gibbs zig-zag sampler. Sachs, M., Sen, D., Lu, J., & Dunson, D. (2023). [arXiv]
Bayesian Analysis {Journal]
Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineups. Barrientos, A.F., Sen, D., Page, G., & Dunson, D. (2023). [arXiv]
Bayesian Analysis [Journal]
Particle filter efficiency under limited communication. Sen, D. (2022). [arXiv]
Biometrika [Journal]
Efficient posterior sampling for high-dimensional imbalanced logistic regression. Sen, D., Sachs, M., Lu, J., & Dunson, D. (2020). [arXiv]
Biometrika [Journal]
On coupling particle filter trajectories. Sen, D., Thiery, A. H., & Jasra, A. (2018). [arXiv]
Statistics and Computing [Journal]
Some contributions to sequential Monte Carlo methods for option pricing. Sen, D., Jasra, A., & Zhou, Y. (2017). [arXiv]
Journal of Statistical Computation and Simulation [Journal]
Preprints
Scalable Bayesian inference for time series via divide-and-conquer. Ou, R., Sen, D., & Dunson, D. (2024+). [arXiv]
(R&R at JASA-T&M)
Posterior projection for inference in constrained spaces. Astfalck, L., Sen, D., Patra, S., Cripps, E., & Dunson, D. (2024+). [arXiv]
(Submitted to SIAM JUQ)
Book Chapter
Bayesian neural networks and dimensionality reduction. Sen, D., Papamarkou, T., & Dunson, D. (2024). [arXiv]
Chapman & Hall’s Handbook on Bayesian, Fiducial, and Frequentist Inference [Book Chapter]