Everything about Iterated filtering
Iterated filtering algorithms are a tool for maximum likelihood inference on partially observed dynamical systems. Stochastic perturbations to the unknown parameters are used to explore the parameter space. Applying sequential Monte Carlo to this extended model results in the selection of the parameter values that are more consistent with the data. Appropriately constructed procedures, iterating with successively diminished perturbations, converge to the maximum likelihood estimate. Iterated filtering methods have so far been used most extensively to study infectious disease transmission dynamics. Case studies include cholera, Ebola virus, influenza, malaria, HIV, pertussis, poliovirus and measles. Other areas which have been proposed to be suitable for these methods include ecological dynamics and finance. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Iterated filteringA 5-minute orientation
- Skim a recent paper: Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification ideaInfluential work since 2022
- Pick up a classic book: Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computerThe most-reprinted book on the subject
- Read the landmark paper: FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman FilterCited 1,170 times
- Go sideways: Point estimationA closely related topic
- Go sideways: Method of moments (statistics)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
Iterated filtering
From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups
Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany
Improving Accuracy by Iterated Multiple Particle Filtering
Robust Huber-Based Iterated Divided Difference Filtering with Application to Cooperative Localization of Autonomous Underwater Vehicles
Iterated partitioned block frequency-domain adaptive filtering for acoustic echo cancellation
Constrained signal restoration via iterated extended Kalman filtering
🆕 Influential since 2022
Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea
Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering
Dynamic Harmonic Estimation Using a Novel Robust Filtering Strategy: Iterated Sliding Innovation Cubature Filter
Iterated rational quadratic kernel - High-order unscented Kalman filtering algorithm for spacecraft tracking
Accelerated Iterated Filtering
📚 Books worth reading
- 📕Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computerGeorge E. Ioup · 1985
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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