Inference for nonlinear dynamical systems

By Edward L. Ionides, Carles Bretó, Aaron M. King. Proceedings of the National Academy of Sciences of the United States of America, 2006

518 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Endocrinology

View Paper: https://www.semanticscholar.org/paper/d0b74491b72fbfd747ffa621d59d9e5fc6c33699

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Citations in the corpus

Bayesian Solution Uncertainty Quantification for Differential Equations (2013, background): “…calibration or statistical inverse problem concerns the uncertainty in unknown model inputs, θ , given measurements of the model states (Bock, 1983; Ionides et al., 2006; Dowd, 2007; Ramsay et al., 2007; Xue et al., 2010; Brunel, 2008; Liang and Wu, 2008; Calderhead and Girolami, 2011; Campbell…”
Truncated Importance Sampling (2008, methodology): “…that employ basic sequential Monte Carlo techniques for parameter estimation by judiciously adding stochasticity to the parameters (Liu and West 2001; Ionides et al. 2006) could be applied in situations such as Section 7, and could also potentially benefit from truncation of importance weights.”
Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015, methodology): “Iterated ﬁltering techniques maximize the likelihood obtained by SMC (Ionides et al. 2006, 2011, 2015).”
The correlated pseudomarginal method (2015, background): “In this context, Markov chain Monte Carlo (MCMC) schemes targeting the joint posterior density of the parameter and latent variables are often inefficient as the parameter and latent variables can be strongly correlated under the posterior, or cannot even be used if only forward simulation of the la”
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, methodology): “In our terminology, stochastic dynamic system is synonymous with partially observed Markov process (Ionides et al. 2006; Bretó et al. 2009); in the statistical literature, these systems are known as state-space models (Shumway & Stoffer 2006).”
Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations (2009, methodology): “There are some alternative procedures to deal with this problem, for example, the iterated filtering (see Ionides, Breto and King [14] and Breto et al.”
Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE (2015, methodology): “r et al., 2014); \likelihood free" or \plug-and-play" methods such as synthetic likelihood (Wood, 2010), approximate Bayesian computation (ABC; Marjoram et al., 2003), or iterated ltering (Ionides et al., 2006); parametric bootstrapping of any of the above ideas, for which simulations from”