# Publications

Nishimura, A., Dunson, D. B., and Lu, J. (2020)

Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods.

*Biometrika*.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.

*Biometrika*.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:1905.12146Nishimura, 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:1810.12437Yang, 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:1604.00889Nishimura, 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.