• Nishimura, A., Dunson, D. B., and Lu, J. (2020)
    Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods.

  • Nishimura, A. and Dunson, D. B. (2020)
    Recycling intermediate steps to improve Hamiltonian Monte Carlo.
    Bayesian Analysis.

  • Duan, L. L., Young, A. L., Nishimura, A., and Dunson, D. B. (2020)
    Bayesian Constraint Relaxation.

  • Zhang, Z., Nishimura, A., Bastide, P., Ji, X., Lemey, P., and Suchard, M. A. (2019)
    Large-scale inference of correlation among mixed-type biological traits with phylogenetic multivariate probit models.
    Preprint at arXiv:1912.09185.

  • Nishimura, A. and Suchard, M. A. (2019)
    Regularization of Bayesian shrinkage priors and inference via geometrically / uniformly ergodic Gibbs sampler.
    Preprint at arXiv:1911.0216.

  • Ji, X. and Zhang, Z. and Holbrook, A. and Nishimura, A. and Fisher, A. and Baele, G. and Suchard, M. A. (2019)
    Gradients do grow on trees: a linear-time O(N)-dimensional gradient for statistical phylogenetics. arXiv:1905.12146

  • Nishimura, A. and Suchard, M. A. (2018)
    Prior-preconditioned conjugate gradient for accelerated Gibbs sampling in “large n & large p” sparse Bayesian logistic regression models. arXiv:1810.12437

  • Yang, H., Nishimura, A., and Lu, Q. (2017)
    Bayesian heteroscedastic matrix factorization for conversion rate prediction.
    26th ACM International Conference on Information and Knowledge Management (CIKM 2017)

  • Nishimura, A. and Dunson, D. B. (2016)
    Variable trajectory length compressible Hamiltonian Monte Carlo. arXiv:1604.00889

  • Nishimura, A. and Dunson, D. B. (2016)
    Geometrically tempered Hamiltonian Monte Carlo. arXiv:1604.00872

In Preparation

  • Nishimura, A., Suchard, M. A., and Schuemie, M. J.
    Scalable Bayesian sparse generalized linear models and survival analysis via curvature-adaptive Hamiltonian Monte Carlo for high-dimensional log-concave distributions.