State filtering parameters

Classic recursive estimation
Iterated filtering
Questions
- Basic Construction
Awaiting filing
Implementations
References

a.k.a. state space model calibration, recursive identification. Sometimes indistinguishable from online estimation.

State filters are cool for estimating time-varying hidden states given known fixed system parameters. How about learning those parameters of the model generating your states? Classic ways that you can do this in dynamical systems include basic linear system identification, and general system identification. But can you identify the fixed parameters (not just hidden states) with a state filter?

Yes.

According to (Lindström et al. 2012), here are some landmark papers:

Augmenting the unobserved state vector is a well known technique, used in the system identification community for decades, see e.g. Ljung (L. Ljung 1979; Lindström et al. 2008; Söderström and Stoica 1988). Similar ideas, using Sequential Monte Carlos methods, were suggested by (Kitagawa 1998; Liu and West 2001). Combined state and parameter estimation is also the standard technique for data assimilation in high-dimensional systems, see Moradkhani et al. (Geir Evensen 2009; G. Evensen 2009; Moradkhani et al. 2005)

However, introducing random walk dynamics to the parameters with fixed variance leads to a new dynamical stochastic system with properties that may be different from the properties of the original system. That implies that the variance of the random walk should be decreased, when the method is used for offline parameter estimation, cf. (Hürzeler and Künsch 2001).

Classic recursive estimation

🏗

Iterated filtering

Related: indirect inference. Precise relation will have to wait, since I currently do not care enough about indirect inference.

Questions

Ionides and King dominate my citations, at least for the frequentist stuff. Surely other people use this method too? But what are the keywords? This research is suspiciously concentrated in U Michigan, but the idea is not so esoteric. I think I am caught in a citation bubble.

Update: the oceanographers, e.g. (Geir Evensen 2003), seem to do this with Bayes a lot.
a lot of the variational filtering literature turns out to be about attempting this with, effectively, neural nets.
can I estimate regularisation this way, despite the lack of probabilistic interpretation? (leveraging Bayesian-prior parameter relations)
How does this work with non-Markov systems? Do we need to bother, or can we just do the Hamiltonian trick and augment the state vector? Can we talk about mixing, or correlation decay? Should I then shoot for the new-wave mixing approaches of Kuznetsov and Mohri etc?

Basic Construction

There are a few variations. We start with the basic continuous time state space model.

Here we have an unobserved Markov state process \(x(t)\) on \(\mathcal{X}\) and an observation process \(y(t)\) on \(\mathcal{Y}\). For now they will be assumed to be finite dimensional vectors over \(\mathbb{R}.\) They will additionally depend upon a vector of parameters \(\theta\) We observe the process at discrete times \(t(1:T)=(t_1, t_2,\dots, t_T),\) and we will write the observations \(y(1:T)=(y(t_1), y(t_2),\dots, y(1_T)).\)

We presume our processes are completely specified by the following conditional densities (which might not have closed-form expression)

The transition density

\[f(x(t_i)|x(t_{i-1}), \theta)\]

The observation density…

TBC.

Awaiting filing

Recently enjoyed: Sahani Pathiraja’s state filter does something cool, in attempting to identify process model noise — a conditional nonparametric density of process errors, that may be used to come up with some neat process models. I’m not convinced about her use of kernel density estimation, since these scale badly precisely when you need them most, in high dimension; but any nonparametric density estimator would, I assume, work, and that would be awesome.

Implementations

pomp does state filtering inference in R.

For some example of doing this in Stan see Sinhrks’ stan-statespace.

References

Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. 2010. “Particle Markov Chain Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72 (3): 269–342. https://doi.org/10.1111/j.1467-9868.2009.00736.x.

Archer, Evan, Il Memming Park, Lars Buesing, John Cunningham, and Liam Paninski. 2015. “Black Box Variational Inference for State Space Models.” November 23, 2015. http://arxiv.org/abs/1511.07367.

Babtie, Ann C., Paul Kirk, and Michael P. H. Stumpf. 2014. “Topological Sensitivity Analysis for Systems Biology.” Proceedings of the National Academy of Sciences 111 (52): 18507–12. https://doi.org/10.1073/pnas.1414026112.

Bamler, Robert, and Stephan Mandt. 2017. “Structured Black Box Variational Inference for Latent Time Series Models.” July 4, 2017. http://arxiv.org/abs/1707.01069.

Becker, Philipp, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor, and Gerhard Neumann. 2019. “Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces.” In International Conference on Machine Learning, 544–52. http://proceedings.mlr.press/v97/becker19a.html.

Bretó, Carles, Daihai He, Edward L. Ionides, and Aaron A. King. 2009. “Time Series Analysis via Mechanistic Models.” The Annals of Applied Statistics 3 (1): 319–48. https://doi.org/10.1214/08-AOAS201.

Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz. 2016. “Discovering Governing Equations from Data by Sparse Identification of Nonlinear Dynamical Systems.” Proceedings of the National Academy of Sciences 113 (15): 3932–37. https://doi.org/10.1073/pnas.1517384113.

Chung, Junyoung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio. 2015. “A Recurrent Latent Variable Model for Sequential Data.” In Advances in Neural Information Processing Systems 28, edited by C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, 2980–88. Curran Associates, Inc. http://papers.nips.cc/paper/5653-a-recurrent-latent-variable-model-for-sequential-data.pdf.

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Doucet, Arnaud, Nando Freitas, and Neil Gordon. 2001. Sequential Monte Carlo Methods in Practice. New York, NY: Springer New York. http://public.eblib.com/choice/publicfullrecord.aspx?p=3087052.

Doucet, Arnaud, Pierre E. Jacob, and Sylvain Rubenthaler. 2013. “Derivative-Free Estimation of the Score Vector and Observed Information Matrix with Application to State-Space Models.” April 21, 2013. http://arxiv.org/abs/1304.5768.

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Evensen, G. 2009. “The Ensemble Kalman Filter for Combined State and Parameter Estimation.” IEEE Control Systems 29 (3): 83–104. https://doi.org/10.1109/MCS.2009.932223.

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Ionides, Edward L., Anindya Bhadra, Yves Atchadé, and Aaron King. 2011. “Iterated Filtering.” The Annals of Statistics 39 (3): 1776–1802. https://doi.org/10.1214/11-AOS886.

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