Export 1510 results:
Campbell, T. & Broderick, T., Submitted. Automated scalable Bayesian inference via Hilbert coresets. arXiv:1710.05053.
Watson, J., V. Zidek, J. & Shaddick, G., Submitted. A general theory for preferential sampling in environmental networks.
Freue, G.V.Cohen et al., Submitted. Proteomic biomarker study using novel robust penalized elastic net estimators. Annals of Applied Statistics.
In Press
Campbell, T., Kulis, B. & How, J., In Press. Dynamic clustering algorithms via small-variance analysis of Markov chain mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Dinsdale, D.R. & Salibian-Barrera, M., In Press. Modelling ocean temperatures from bio-probes under preferential sampling. Annals of Applied Statistics. Available at: https://arxiv.org/abs/1901.02630.
Zhu, G. & Chen, J., In Press. Multi-parameter One-Sided Monitoring Tests. Technometrics.
Lennox, R.J. et al., In Press. Optimizing marine spatial plans with animal tracking data. Canadian Journal of Fisheries and Aquatic Sciences.
Chen, J. & Liu, Y., In Press. Small Area Quantile Estimation. International Statistics Review.
Campbell, T. et al., In Press. Truncated random measures. Bernoulli.
Wang, L., Wang, S. & Bouchard-Côté, A., 2019. An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. Systematic Biology, (Accepted).
Chang, B. et al., 2019. AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. In International Conference on Learning Representations. International Conference on Learning Representations. Available at: https://openreview.net/forum?id=ryxepo0cFX.
Watson, J., 2019. CV.
Wong, S.W.K. & Zidek, J.V., 2019. The duration of load effect in lumber as stochastic degradation. IEEE Transactions on Reliability, p.To appear.
Deligiannidis, G., Bouchard-Côté, A. & Doucet, A., 2019. Exponential Ergodicity of the Bouncy Particle Sampler. Annals of Statistics, 47, pp.1268–1287.
Dinsdale, D.R. & Salibian-Barrera, M., 2019. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C, 68(1), p.198. Available at: https://dx.doi.org/10.1111/rssc.12286 .
Fu, E. & Heckman, N., 2019. Model-based curve registration via stochastic approximation EM algorithm. Computational Statistics and Data Analysis, 131. Available at: https://arxiv.org/abs/1712.07265.
Chang, B., Pan, S. & Joe, H., 2019. Vine Copula Structure Learning via Monte Carlo Tree Search. In International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics.
Yang, C.-H., Zidek, J.V. & Wong, S.W.K., 2018. Bayesian analysis of accumulated damage models in lumber reliability. Technometrics.
Campbell, T. & Broderick, T., 2018. Bayesian coreset construction via greedy iterative geodesic ascent. In International Conference on Machine Learning. International Conference on Machine Learning.
Xia, M. & Gustafson, P., 2018. Bayesian inference for unidirectional misclassification of a binary response trait. Statistics in medicine, 37, pp.933–947.
Wang, W. & Welch, W.J., 2018. Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter Tuning. In Proceedings of AutoML 2018 @ ICML/IJCAI-ECAI. Proceedings of AutoML 2018 @ ICML/IJCAI-ECAI. Available at: https://sites.google.com/site/automl2018icml/accepted-papers/59.pdf.
Kondo, Y. et al., 2018. Bayesian subset selection procedures with an application to lumber strength properties. Sankhya Ser A, p.Accepted Aug 08, 2018.