publication . Preprint . 2018

Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter

Pulido, Manuel; vanLeeuwen, Peter Jan;
Open Access English
  • Published: 29 May 2018
Abstract
Comment: Submitted to PNAS
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Learning, Mathematics - Optimization and Control, Physics - Atmospheric and Oceanic Physics
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28 references, page 1 of 2

Angenent, S., Haker, S. and Tannenbaum, A. (2003) Minimizing flows for the MongeKantorovich problem. SIAM journal on mathematical analysis, 35, 61-97. [OpenAIRE]

Arulampalam, M.S., Maskell, S., Gordon, N. and Clapp, T. (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on signal processing, 50, 174-188.

Atkins, E., Morzfeld, M. and Chorin, A.J., (2013) Implicit particle methods and their connection with variational data assimilation. Mon. Wea. Rev., 141, 1786-1803.

Bunch, P. and Godsill, S., (2016) Approximations of the optimal importance density using Gaussian particle flow importance sampling. Journal of the American Statistical Association, 111, 748-762. [OpenAIRE]

Capp´e O, Moulines E, Rydon T. (2005) Inference in Hidden Markov models. Springer Science+Business Media: New York, NY. [OpenAIRE]

Cheng, Y. and Reich, S., (2015) Assimilating data into scientific models: An optimal coupling perspective. In Nonlinear Data Assimilation, 75-118. Springer.

Chorin, A.J. and Tu, X., (2009) Implicit sampling for particle filters. Proc. Nat. Acad. Sci. USA, 106, 17249-17254. [OpenAIRE]

Daum, F. and Huang, J., (2007) Nonlinear filters with log-homotopy. In Signal and Data Processing of Small Targets 2007. 6699, p. 669918.

Doucet, A., Godsill, S. and Andrieu, C. (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and computing, 10, 197-208.

Ionides, E.L., Bret´o, C. and King, A.A. (2006) Inference for nonlinear dynamical systems. Proc. Nat. Acad. Sci. USA, 103, 18438-18443. [OpenAIRE]

Kingma, D. and Ba, J. (2015) Adam: A method for stochastic optimization. In Int. Conf. on Learning Repres. (ICLR) arXiv preprint arXiv:1412.6980.

LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. Nature, 521, 436-444.

Li, Y. and Coates, M., (2017) Particle filtering with invertible particle flow. IEEE Transactions on Signal Processing, 65, 4102-4116.

Liu, Q. and Wang, D. (2016) Stein variational gradient descent: A general purpose bayesian inference algorithm. In Advances In Neural Information Processing Systems, 2378-2386.

Liu, J. and West, M., (2001) Combined parameter and state estimation in simulation-based filtering. In Sequential Monte Carlo methods in practice, 197-223. Springer, New York.

28 references, page 1 of 2
Related research
Abstract
Comment: Submitted to PNAS
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Learning, Mathematics - Optimization and Control, Physics - Atmospheric and Oceanic Physics
Related Organizations
Download from
28 references, page 1 of 2

Angenent, S., Haker, S. and Tannenbaum, A. (2003) Minimizing flows for the MongeKantorovich problem. SIAM journal on mathematical analysis, 35, 61-97. [OpenAIRE]

Arulampalam, M.S., Maskell, S., Gordon, N. and Clapp, T. (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on signal processing, 50, 174-188.

Atkins, E., Morzfeld, M. and Chorin, A.J., (2013) Implicit particle methods and their connection with variational data assimilation. Mon. Wea. Rev., 141, 1786-1803.

Bunch, P. and Godsill, S., (2016) Approximations of the optimal importance density using Gaussian particle flow importance sampling. Journal of the American Statistical Association, 111, 748-762. [OpenAIRE]

Capp´e O, Moulines E, Rydon T. (2005) Inference in Hidden Markov models. Springer Science+Business Media: New York, NY. [OpenAIRE]

Cheng, Y. and Reich, S., (2015) Assimilating data into scientific models: An optimal coupling perspective. In Nonlinear Data Assimilation, 75-118. Springer.

Chorin, A.J. and Tu, X., (2009) Implicit sampling for particle filters. Proc. Nat. Acad. Sci. USA, 106, 17249-17254. [OpenAIRE]

Daum, F. and Huang, J., (2007) Nonlinear filters with log-homotopy. In Signal and Data Processing of Small Targets 2007. 6699, p. 669918.

Doucet, A., Godsill, S. and Andrieu, C. (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and computing, 10, 197-208.

Ionides, E.L., Bret´o, C. and King, A.A. (2006) Inference for nonlinear dynamical systems. Proc. Nat. Acad. Sci. USA, 103, 18438-18443. [OpenAIRE]

Kingma, D. and Ba, J. (2015) Adam: A method for stochastic optimization. In Int. Conf. on Learning Repres. (ICLR) arXiv preprint arXiv:1412.6980.

LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. Nature, 521, 436-444.

Li, Y. and Coates, M., (2017) Particle filtering with invertible particle flow. IEEE Transactions on Signal Processing, 65, 4102-4116.

Liu, Q. and Wang, D. (2016) Stein variational gradient descent: A general purpose bayesian inference algorithm. In Advances In Neural Information Processing Systems, 2378-2386.

Liu, J. and West, M., (2001) Combined parameter and state estimation in simulation-based filtering. In Sequential Monte Carlo methods in practice, 197-223. Springer, New York.

28 references, page 1 of 2
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