Research Interests:
- Bayesian nonparametrics:
Dependent nonparametric models, MCMC methods and deterministic approximations for efficient inference in nonparametric models
- Continuous time stochastic processes:
MCMC methods for inference in Markov jump processes and continuous time Bayesian networks
- Point processes:
Nonstationary renewal processes and repulsive point processes
- Markov chain Monte Carlo for doubly intractable problems
- Machine learning
Selected publications:
- Awan, J., and Rao, V.A. (2021)
Privacy-aware rejection sampling
[arxiv:2108.00965]
- Jaiswal, P., Rao, V.A. and Honnappa, H. (2020)
Asymptotic Consistency of α-Renyi-Approximate Posteriors
Journal of Machine Learning Research (accepted)
[arxiv:1902.01902]
- Zhang, B. and Rao, V.A. (2020)
Efficient parameter sampling for Markov jump processes
Journal of Computational and Graphical Statistics (accepted)
[arxiv:1704.02369]
- Yang, J., Rao, V.A., and Neville, J. (2019)
A Stein-Papangelou Goodness-of-Fit Test for Point Processes.
Artificial Intelligence and Statistics (AISTATS 2019) (oral)
[pdf]
- Rao, V.A., Adams,R.P. and Dunson, D.B. (2016)
Bayesian inference for Matérn repulsive processes
Journal of the Royal Statistical Society, Series-B
[arxiv:1308.1136]
[supplementary]
[bibtex]
- Rao, V.A., Lin, L. and Dunson, D.B. (2016)
Data augmentation for models based on rejection sampling
Biometrika
[Biometrika]
[bibtex]