Agarwal, Anish, Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. 2018.
βTime Series Analysis via Matrix Estimation.β arXiv:1802.09064 [Cs, Stat], February.
Alzraiee, Ayman H., Jeremy T. White, Matthew J. Knowling, Randall J. Hunt, and Michael N. Fienen. 2022.
βA Scalable Model-Independent Iterative Data Assimilation Tool for Sequential and Batch Estimation of High Dimensional Model Parameters and States.β Environmental Modelling & Software 150 (April): 105284.
Andersson, Joel A. E., Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. 2019.
βCasADi: A Software Framework for Nonlinear Optimization and Optimal Control.β Mathematical Programming Computation 11 (1): 1β36.
Andrews, Donald W. K. 1994.
βEmpirical Process Methods in Econometrics.β In
Handbook of Econometrics, edited by Robert F. Engle and Daniel L. McFadden, 4:2247β94. Elsevier.
Antoniano-Villalobos, Isadora, and Stephen G. Walker. 2016.
βA Nonparametric Model for Stationary Time Series.β Journal of Time Series Analysis 37 (1): 126β42.
Arridge, Simon, Peter Maass, Ozan Γktem, and Carola-Bibiane SchΓΆnlieb. 2019.
βSolving Inverse Problems Using Data-Driven Models.β Acta Numerica 28 (May): 1β174.
Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp. 2002.
βA Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking.β IEEE Transactions on Signal Processing 50 (2): 174β88.
Ben Taieb, Souhaib, and Amir F. Atiya. 2016.
βA Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.β IEEE transactions on neural networks and learning systems 27 (1): 62β76.
Bengio, Samy, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015.
βScheduled Sampling for Sequence Prediction with Recurrent Neural Networks.β In
Advances in Neural Information Processing Systems 28, 1171β79. NIPSβ15. Cambridge, MA, USA: Curran Associates, Inc.
Berry, Tyrus, Dimitrios Giannakis, and John Harlim. 2020.
βBridging Data Science and Dynamical Systems Theory.β arXiv:2002.07928 [Physics, Stat], June.
Bosq, Denis. 1998. Nonparametric Statistics for Stochastic Processes: Estimation and Prediction. 2nd ed. Lecture Notes in Statistics 110. New York: Springer.
Bosq, Denis, and Delphine Blanke. 2007. Inference and prediction in large dimensions. Wiley series in probability and statistics. Chichester, England ; Hoboken, NJ: John Wiley/Dunod.
BretΓ³, Carles, Daihai He, Edward L. Ionides, and Aaron A. King. 2009.
βTime Series Analysis via Mechanistic Models.β The Annals of Applied Statistics 3 (1): 319β48.
Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz. 2016.
βDiscovering Governing Equations from Data by Sparse Identification of Nonlinear Dynamical Systems.β Proceedings of the National Academy of Sciences 113 (15): 3932β37.
BΓΌhlmann, Peter, and Hans R KΓΌnsch. 1999.
βBlock Length Selection in the Bootstrap for Time Series.β Computational Statistics & Data Analysis 31 (3): 295β310.
Campbell, Andrew, Yuyang Shi, Tom Rainforth, and Arnaud Doucet. 2021.
βOnline Variational Filtering and Parameter Learning.β In.
Carmi, Avishy Y. 2014.
βCompressive System Identification.β In
Compressed Sensing & Sparse Filtering, edited by Avishy Y. Carmi, Lyudmila Mihaylova, and Simon J. Godsill, 281β324. Signals and Communication Technology. Springer Berlin Heidelberg.
Cassidy, Ben, Caroline Rae, and Victor Solo. 2015.
βBrain Activity: Connectivity, Sparsity, and Mutual Information.β IEEE Transactions on Medical Imaging 34 (4): 846β60.
Chan, Ngai Hang, Ye Lu, and Chun Yip Yau. 2016.
βFactor Modelling for High-Dimensional Time Series: Inference and Model Selection.β Journal of Time Series Analysis, January, n/aβ.
Chen, Chong, Yixuan Dou, Jie Chen, and Yaru Xue. 2022.
βA Novel Neural Network Training Framework with Data Assimilation.β The Journal of Supercomputing, June.
Chen, Tian Qi, and David K Duvenaud. n.d. βNeural Networks with Cheap Differential Operators,β 11.
Chen, Tian Qi, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018.
βNeural Ordinary Differential Equations.β In
Advances in Neural Information Processing Systems 31, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, 6572β83. Curran Associates, Inc.
Chevillon, Guillaume. 2007.
βDirect Multi-Step Estimation and Forecasting.β Journal of Economic Surveys 21 (4): 746β85.
Choromanski, Krzysztof, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, and Vikas Sindhwani. 2020.
βAn Ode to an ODE.β In
Advances in Neural Information Processing Systems. Vol. 33.
Cook, Alex R., Wilfred Otten, Glenn Marion, Gavin J. Gibson, and Christopher A. Gilligan. 2007.
βEstimation of Multiple Transmission Rates for Epidemics in Heterogeneous Populations.β Proceedings of the National Academy of Sciences 104 (51): 20392β97.
Corenflos, Adrien, James Thornton, George Deligiannidis, and Arnaud Doucet. 2021.
βDifferentiable Particle Filtering via Entropy-Regularized Optimal Transport.β arXiv:2102.07850 [Cs, Stat], June.
Course, Kevin, Trefor Evans, and Prasanth Nair. 2020.
βWeak Form Generalized Hamiltonian Learning.β In
Advances in Neural Information Processing Systems. Vol. 33.
De Brouwer, Edward, Jaak Simm, Adam Arany, and Yves Moreau. 2019.
βGRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series.β In
Advances in Neural Information Processing Systems. Vol. 32. Curran Associates, Inc.
βββ. 2012. Time Series Analysis by State Space Methods. 2nd ed. Oxford Statistical Science Series 38. Oxford: Oxford University Press.
E, Weinan, Jiequn Han, and Qianxiao Li. 2018.
βA Mean-Field Optimal Control Formulation of Deep Learning.β arXiv:1807.01083 [Cs, Math], July.
Eden, U, L Frank, R Barbieri, V Solo, and E Brown. 2004.
βDynamic Analysis of Neural Encoding by Point Process Adaptive Filtering.β Neural Computation 16 (5): 971β98.
Errico, Ronald M. 1997.
βWhat Is an Adjoint Model?β Bulletin of the American Meteorological Society 78 (11): 2577β92.
Evensen, Geir. 2009.
Data Assimilation - The Ensemble Kalman Filter. Berlin; Heidelberg: Springer.
Evensen, Geir, and Peter Jan van Leeuwen. 2000.
βAn Ensemble Kalman Smoother for Nonlinear Dynamics.β Monthly Weather Review 128 (6): 1852β67.
Fan, Jianqing, and Qiwei Yao. 2003. Nonlinear Time Series: Nonparametric and Parametric Methods. Springer Series in Statistics. New York: Springer.
Fearnhead, Paul, and Hans R. KΓΌnsch. 2018.
βParticle Filters and Data Assimilation.β Annual Review of Statistics and Its Application 5 (1): 421β49.
Finlay, Chris, JΓΆrn-Henrik Jacobsen, Levon Nurbekyan, and Adam M Oberman. n.d. βHow to Train Your Neural ODE: The World of Jacobian and Kinetic Regularization.β In ICML, 14.
Finzi, Marc, Ke Alexander Wang, and Andrew G. Wilson. 2020.
βSimplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints.β In
Advances in Neural Information Processing Systems. Vol. 33.
Flunkert, Valentin, David Salinas, and Jan Gasthaus. 2017.
βDeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks.β arXiv:1704.04110 [Cs, Stat], April.
Fraser, Andrew M. 2008. Hidden Markov Models and Dynamical Systems. Philadelphia, PA: Society for Industrial and Applied Mathematics.
Gholami, Amir, Kurt Keutzer, and George Biros. 2019.
βANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs.β arXiv:1902.10298 [Cs], February.
Ghosh, Arnab, Harkirat Behl, Emilien Dupont, Philip Torr, and Vinay Namboodiri. 2020.
βSTEER : Simple Temporal Regularization For Neural ODE.β In
Advances in Neural Information Processing Systems. Vol. 33.
Gorad, Ajinkya, Zheng Zhao, and Simo SΓ€rkkΓ€. 2020. βParameter Estimation in Non-Linear State-Space Models by Automatic Differentiation of Non-Linear Kalman Filters.β In, 6.
Grathwohl, Will, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. 2018.
βFFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models.β arXiv:1810.01367 [Cs, Stat], October.
Gu, Albert, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher RΓ©. 2021.
βCombining Recurrent, Convolutional, and Continuous-Time Models with Linear State Space Layers.β In
Advances in Neural Information Processing Systems, 34:572β85. Curran Associates, Inc.
Harvey, A., and S. J. Koopman. 2005.
βStructural Time Series Models.β In
Encyclopedia of Biostatistics. John Wiley & Sons, Ltd.
Hazan, Elad, Karan Singh, and Cyril Zhang. 2017.
βLearning Linear Dynamical Systems via Spectral Filtering.β In
NIPS.
He, Daihai, Edward L. Ionides, and Aaron A. King. 2010.
βPlug-and-Play Inference for Disease Dynamics: Measles in Large and Small Populations as a Case Study.β Journal of The Royal Society Interface 7 (43): 271β83.
Hefny, Ahmed, Carlton Downey, and Geoffrey Gordon. 2015.
βA New View of Predictive State Methods for Dynamical System Learning.β arXiv:1505.05310 [Cs, Stat], May.
Hirsh, Seth M., David A. Barajas-Solano, and J. Nathan Kutz. 2022.
βSparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery.β Royal Society Open Science 9 (2): 211823.
Holzschuh, Benjamin, Simona Vegetti, and Nils Thuerey. 2022. βScore Matching via Differentiable Physics,β 7.
Hong, X., R. J. Mitchell, S. Chen, C. J. Harris, K. Li, and G. W. Irwin. 2008.
βModel Selection Approaches for Non-Linear System Identification: A Review.β International Journal of Systems Science 39 (10): 925β46.
Houtekamer, P. L., and Fuqing Zhang. 2016.
βReview of the Ensemble Kalman Filter for Atmospheric Data Assimilation.β Monthly Weather Review 144 (12): 4489β4532.
Ionides, E. L., C. BretΓ³, and A. A. King. 2006.
βInference for Nonlinear Dynamical Systems.β Proceedings of the National Academy of Sciences 103 (49): 18438β43.
Ionides, Edward L., Anindya Bhadra, Yves AtchadΓ©, and Aaron King. 2011.
βIterated Filtering.β The Annals of Statistics 39 (3): 1776β1802.
Jia, Junteng, and Austin R Benson. 2019.
βNeural Jump Stochastic Differential Equations.β In
Advances in Neural Information Processing Systems 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d AlchΓ©-Buc, E. Fox, and R. Garnett, 9847β58. Curran Associates, Inc.
Jonschkowski, Rico, Divyam Rastogi, and Oliver Brock. 2018.
βDifferentiable Particle Filters: End-to-End Learning with Algorithmic Priors.β arXiv:1805.11122 [Cs, Stat], May.
Kalli, Maria, and Jim E. Griffin. 2018.
βBayesian Nonparametric Vector Autoregressive Models.β Journal of Econometrics 203 (2): 267β82.
Kantas, Nikolas, Arnaud Doucet, Sumeetpal S. Singh, Jan Maciejowski, and Nicolas Chopin. 2015.
βOn Particle Methods for Parameter Estimation in State-Space Models.β Statistical Science 30 (3): 328β51.
Kantz, Holger, and Thomas Schreiber. 2004. Nonlinear Time Series Analysis. 2nd ed. Cambridge, UK ; New York: Cambridge University Press.
Kass, Robert E., Shun-Ichi Amari, Kensuke Arai, Emery N. Brown, Casey O. Diekman, Markus Diesmann, Brent Doiron, et al. 2018.
βComputational Neuroscience: Mathematical and Statistical Perspectives.β Annual Review of Statistics and Its Application 5 (1): 183β214.
Kelly, Jacob, Jesse Bettencourt, Matthew James Johnson, and David Duvenaud. 2020.
βLearning Differential Equations That Are Easy to Solve.β In.
Kemerait, R., and D. Childers. 1972.
βSignal Detection and Extraction by Cepstrum Techniques.β IEEE Transactions on Information Theory 18 (6): 745β59.
Kendall, Bruce E., Stephen P. Ellner, Edward McCauley, Simon N. Wood, Cheryl J. Briggs, William W. Murdoch, and Peter Turchin. 2005.
βPopulation Cycles in the Pine Looper Moth: Dynamical Tests of Mechanistic Hypotheses.β Ecological Monographs 75 (2): 259β76.
Kidger, Patrick, Ricky T Q Chen, and Terry Lyons. 2020. ββHey, Thatβs Not an ODEβ: Faster ODE Adjoints with 12 Lines of Code.β In, 5.
Kidger, Patrick, James Foster, Xuechen Li, Harald Oberhauser, and Terry Lyons. 2020. βNeural SDEs Made Easy: SDEs Are Inο¬nite-Dimensional GANS.β In Advances In Neural Information Processing Systems, 6.
Kidger, Patrick, James Morrill, James Foster, and Terry Lyons. 2020.
βNeural Controlled Differential Equations for Irregular Time Series.β arXiv:2005.08926 [Cs, Stat], November.
Kitagawa, Genshiro. 1987.
βNon-Gaussian StateβSpace Modeling of Nonstationary Time Series.β Journal of the American Statistical Association 82 (400): 1032β41.
βββ. 1996.
βMonte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models.β Journal of Computational and Graphical Statistics 5 (1): 1β25.
Kitagawa, Genshiro, and Will Gersch. 1996.
Smoothness Priors Analysis of Time Series. Lecture notes in statistics 116. New York, NY: Springer New York : Imprint : Springer.
Kovachki, Nikola B., and Andrew M. Stuart. 2019.
βEnsemble Kalman Inversion: A Derivative-Free Technique for Machine Learning Tasks.β Inverse Problems 35 (9): 095005.
Krishnamurthy, Kamesh, Tankut Can, and David J. Schwab. 2020.
βTheory of Gating in Recurrent Neural Networks.β In
arXiv:2007.14823 [Cond-Mat, Physics:nlin, q-Bio].
Lamb, Alex, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, and Yoshua Bengio. 2016.
βProfessor Forcing: A New Algorithm for Training Recurrent Networks.β In
Advances In Neural Information Processing Systems.
Levin, David N. 2017.
βThe Inner Structure of Time-Dependent Signals.β arXiv:1703.08596 [Cs, Math, Stat], March.
Li, Xuechen, Ting-Kam Leonard Wong, Ricky T. Q. Chen, and David Duvenaud. 2020.
βScalable Gradients for Stochastic Differential Equations.β In
International Conference on Artificial Intelligence and Statistics, 3870β82. PMLR.
Ljung, Lennart. 2010.
βPerspectives on System Identification.β Annual Reviews in Control 34 (1): 1β12.
Lou, Aaron, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser Nam Lim, and Christopher M. De Sa. 2020.
βNeural Manifold Ordinary Differential Equations.β In
Advances in Neural Information Processing Systems. Vol. 33.
Lu, Peter Y., Joan AriΓ±o, and Marin SoljaΔiΔ. 2021.
βDiscovering Sparse Interpretable Dynamics from Partial Observations.β arXiv:2107.10879 [Physics], July.
Massaroli, Stefano, Michael Poli, Jinkyoo Park, Atsushi Yamashita, and Hajime Asama. 2020.
βDissecting Neural ODEs.β In
arXiv:2002.08071 [Cs, Stat].
Mitchell, Herschel L., and P. L. Houtekamer. 2000.
βAn Adaptive Ensemble Kalman Filter.β Monthly Weather Review 128 (2): 416.
Morrill, James, Patrick Kidger, Cristopher Salvi, James Foster, and Terry Lyons. 2020. βNeural CDEs for Long Time Series via the Log-ODE Method.β In, 5.
Nerrand, O., P. Roussel-Ragot, L. Personnaz, G. Dreyfus, and S. Marcos. 1993.
βNeural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms.β Neural Computation 5 (2): 165β99.
Nguyen, Long, and Andy Malinsky. 2020. βExploration and Implementation of Neural Ordinary Diο¬erential Equations,β 34.
Pereyra, M., P. Schniter, Γ Chouzenoux, J. C. Pesquet, J. Y. Tourneret, A. O. Hero, and S. McLaughlin. 2016.
βA Survey of Stochastic Simulation and Optimization Methods in Signal Processing.β IEEE Journal of Selected Topics in Signal Processing 10 (2): 224β41.
Pham, Tung, and Victor Panaretos. 2016.
βMethodology and Convergence Rates for Functional Time Series Regression.β arXiv:1612.07197 [Math, Stat], December.
Pillonetto, Gianluigi. 2016.
βThe Interplay Between System Identification and Machine Learning.β arXiv:1612.09158 [Cs, Stat], December.
Plis, Sergey, David Danks, and Jianyu Yang. 2015.
βMesochronal Structure Learning.β Uncertainty in Artificial Intelligence : Proceedings of the β¦ Conference. Conference on Uncertainty in Artificial Intelligence 31 (July).
Poli, Michael, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park. 2020.
βTorchDyn: A Neural Differential Equations Library.β arXiv:2009.09346 [Cs], September.
Pugachev, V. S., and I. N. SinitοΈ sοΈ‘yn. 2001. Stochastic systems: theory and applications. River Edge, NJ: World Scientific.
Rackauckas, Christopher, Yingbo Ma, Vaibhav Dixit, Xingjian Guo, Mike Innes, Jarrett Revels, Joakim Nyberg, and Vijay Ivaturi. 2018.
βA Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions.β arXiv:1812.01892 [Cs], December.
Rackauckas, Christopher, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, Ali Ramadhan, and Alan Edelman. 2020.
βUniversal Differential Equations for Scientific Machine Learning.β arXiv:2001.04385 [Cs, Math, q-Bio, Stat], August.
Robinson, P. M. 1983.
βNonparametric Estimators for Time Series.β Journal of Time Series Analysis 4 (3): 185β207.
Roeder, Geoffrey, Paul K. Grant, Andrew Phillips, Neil Dalchau, and Edward Meeds. 2019.
βEfficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems.β arXiv:1905.12090 [Cs, Stat], May.
Routtenberg, Tirza, and Joseph Tabrikian. 2010.
βBlind MIMO-AR System Identification and Source Separation with Finite-Alphabet.β IEEE Transactions on Signal Processing 58 (3): 990β1000.
Runge, Jakob, Reik V. Donner, and JΓΌrgen Kurths. 2015.
βOptimal Model-Free Prediction from Multivariate Time Series.β Physical Review E 91 (5).
Ruthotto, Lars, and Eldad Haber. 2018.
βDeep Neural Networks Motivated by Partial Differential Equations.β arXiv:1804.04272 [Cs, Math, Stat], April.
SΓ€rkkΓ€, Simo. 2007.
βOn Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems.β IEEE Transactions on Automatic Control 52 (9): 1631β41.
Sattar, Yahya, and Samet Oymak. 2022.
βNon-Asymptotic and Accurate Learning of Nonlinear Dynamical Systems.β Journal of Machine Learning Research 23 (140): 1β49.
Schillings, Claudia, and Andrew M. Stuart. 2017.
βAnalysis of the Ensemble Kalman Filter for Inverse Problems.β SIAM Journal on Numerical Analysis 55 (3): 1264β90.
Schirmer, Mona, Mazin Eltayeb, Stefan Lessmann, and Maja Rudolph. 2022.
βModeling Irregular Time Series with Continuous Recurrent Units.β arXiv.
Schmidt, Jonathan, Nicholas KrΓ€mer, and Philipp Hennig. 2021.
βA Probabilistic State Space Model for Joint Inference from Differential Equations and Data.β arXiv:2103.10153 [Cs, Stat], June.
Schneider, Tapio, Andrew M. Stuart, and Jin-Long Wu. 2022.
βEnsemble Kalman Inversion for Sparse Learning of Dynamical Systems from Time-Averaged Data.β Journal of Computational Physics 470 (December): 111559.
SjΓΆberg, Jonas, Qinghua Zhang, Lennart Ljung, Albert Benveniste, Bernard Delyon, Pierre-Yves Glorennec, HΓ₯kan Hjalmarsson, and Anatoli Juditsky. 1995.
βNonlinear Black-Box Modeling in System Identification: A Unified Overview.β Automatica, Trends in System Identification, 31 (12): 1691β1724.
StΓ€dler, Nicolas, and Sach Mukherjee. 2013.
βPenalized Estimation in High-Dimensional Hidden Markov Models with State-Specific Graphical Models.β The Annals of Applied Statistics 7 (4): 2157β79.
Stroud, Jonathan R., Matthias Katzfuss, and Christopher K. Wikle. 2018.
βA Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation.β Monthly Weather Review 146 (1): 373β86.
Stroud, Jonathan R., Michael L. Stein, Barry M. Lesht, David J. Schwab, and Dmitry Beletsky. 2010.
βAn Ensemble Kalman Filter and Smoother for Satellite Data Assimilation.β Journal of the American Statistical Association 105 (491): 978β90.
Tallec, Corentin, and Yann Ollivier. 2017.
βUnbiasing Truncated Backpropagation Through Time.β arXiv:1705.08209 [Cs], May.
Taniguchi, Masanobu, and Yoshihide Kakizawa. 2000. Asymptotic Theory of Statistical Inference for Time Series. Springer Series in Statistics. New York: Springer.
Tanizaki, Hisashi. 2001.
βEstimation of Unknown Parameters in Nonlinear and Non-Gaussian State-Space Models.β Journal of Statistical Planning and Inference 96 (2): 301β23.
Unser, Michael A., and Pouya Tafti. 2014.
An Introduction to Sparse Stochastic Processes. New York: Cambridge University Press.
Wedig, W. 1984.
βA Critical Review of Methods in Stochastic Structural Dynamics.β Nuclear Engineering and Design 79 (3): 281β87.
Wen, Ruofeng, Kari Torkkola, and Balakrishnan Narayanaswamy. 2017.
βA Multi-Horizon Quantile Recurrent Forecaster.β arXiv:1711.11053 [Stat], November.
Williams, Ronald J., and David Zipser. 1989.
βA Learning Algorithm for Continually Running Fully Recurrent Neural Networks.β Neural Computation 1 (2): 270β80.
Yang, Biao, Jonathan R. Stroud, and Gabriel Huerta. 2018.
βSequential Monte Carlo Smoothing with Parameter Estimation.β Bayesian Analysis 13 (4): 1137β61.
Zammit-Mangion, Andrew, and Christopher K. Wikle. 2020.
βDeep Integro-Difference Equation Models for Spatio-Temporal Forecasting.β Spatial Statistics 37 (June): 100408.
Zhang, Han, Xi Gao, Jacob Unterman, and Tom Arodz. 2020.
βApproximation Capabilities of Neural ODEs and Invertible Residual Networks.β arXiv:1907.12998 [Cs, Stat], February.
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