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:

  • 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]