Publications

Journal articles

1. 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]

2. Posterior computation with the Gibbs zig-zag sampler.  Sachs, M., Sen, D., Lu, J., & Dunson, D. (2023). [arXiv]
   Bayesian Analysis {Journal]

3. 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] 

4. Particle filter efficiency under limited communication.  Sen, D. (2022). [arXiv]
   Biometrika [Journal]

5. Efficient posterior sampling for high-dimensional imbalanced logistic regression. Sen, D., Sachs, M., Lu, J., & Dunson, D. (2020). [arXiv] 
   Biometrika [Journal] 

6. On coupling particle filter trajectories. Sen, D., Thiery, A. H., & Jasra, A. (2018). [arXiv]
   Statistics and Computing [Journal] 

7. 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]

Submitted articles

8. Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states. Ou, R., Sen, D., Young, A.L., & Dunson, D. (2022+). [arXiv]  
   (Reject and resubmit at Bayesian Analysis) 

9. Scalable Bayesian inference for time series via divide-and-conquer. Ou, R., Sen, D., & Dunson, D. (2022+).  [arXiv] 
   (Reject and resubmit at JASA-T&M)

10. Constrained Bayesian inference through posterior projections. Sen, D., Patra, S., & Dunson, D. (2022+). [arXiv] 
    (Reject and resubmit at Bayesian Analysis) 

Book Chapter

1. Bayesian neural networks and dimensionality reduction. Sen, D., Papamarkou, T., & Dunson, D. (2022). [arXiv]  
   (To appear in Chapman & Hall’s Handbook on Bayesian, Fiducial, and Frequentist Inference)