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Nonlinear input design as optimal control of a Hamiltonian system., и . CoRR, (2019)Data-Driven Impulse Response Regularization via Deep Learning., , и . CoRR, (2018)Constructing the Matrix Multilayer Perceptron and its Application to the VAE., , , и . CoRR, (2019)Nested Sequential Monte Carlo Methods., , и . ICML, том 37 из JMLR Workshop and Conference Proceedings, стр. 1292-1301. JMLR.org, (2015)Prediction Performance After Learning in Gaussian Process Regression., , , и . AISTATS, том 54 из Proceedings of Machine Learning Research, стр. 1264-1272. PMLR, (2017)Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models., , , и . ICASSP, стр. 6288-6292. IEEE, (2013)Elements of Sequential Monte Carlo., , и . Foundations and Trends in Machine Learning, 12 (3): 307-392 (2019)Optimal controller/observer gains of discounted-cost LQG systems., и . Autom., (2019)Identification of Hammerstein-Wiener models., , , и . Autom., 49 (1): 70-81 (2013)Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models, и . (2015)cite arxiv:1511.01707v4.pdfComment: 36 pages, 8 figures. Submitted to Journal of Statisical Software. Fixed typos and made minior revisions. Source code for R, Python and MATLAB available at: https://github.com/compops/pmh-tutorial.