Ensemble smoothers for inference of hidden states and parameters in combinatorial regulatory model

DOI10.1016/J.JFRANKLIN.2019.10.015zbMath1453.62633OpenAlexW2981918455MaRDI QIDQ1989300

Satoru Miyano, Rui Yamaguchi, Takanori Hasegawa, Atsushi Niida, Seiya Imoto

Publication date: 21 April 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2019.10.015

zbMATH Keywords

ensemble Kalman filter (EnKF)inference of hidden states

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05)

Uses Software

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Cites Work

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• Bridging the ensemble Kalman filter and particle filters: The adaptive Gaussian mixture filter
• Estimating the dimension of a model
• The variational Kalman filter and an efficient implementation using limited memory BFGS
• CAN MARKOV CHAIN MODELS MIMIC BIOLOGICAL REGULATION?
• Monte Carlo Smoothing for Nonlinear Time Series

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