Particle filters for partially-observed Boolean dynamical systems

DOI10.1016/j.automatica.2017.10.009zbMath1378.93126arXiv1702.07269OpenAlexW2592482446WikidataQ62102695 ScholiaQ62102695MaRDI QIDQ680526

Mahdi Imani, Ulisses M. Braga-Neto

Publication date: 23 January 2018

Published in: IEEE Transactions on Signal Processing, Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1702.07269

zbMATH Keywords

adaptive filteringgene regulatory networksexpectation maximizationmaximum-likelihood estimationauxiliary particle-filterBoolean Kalman filterfixed-interval smootherpartially-observed Boolean dynamical systemsRNA-seq data

Mathematics Subject Classification ID

Related Items (9)

Finite-horizon LQR controller for partially-observed Boolean dynamical systems ⋮ Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity ⋮ Parameter learning of stochastic Boolean networks ⋮ State estimation of Boolean control networks under stochastic disturbances with random delay in measurements ⋮ Particle filters for partially-observed Boolean dynamical systems ⋮ Multi-vehicle tracking with microscopic traffic flow model-based particle filtering ⋮ Boolean Kalman filter and smoother under model uncertainty ⋮ Optimal reconstruction of noisy dynamics and selection probabilities in Boolean networks ⋮ Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems

Uses Software

• CRAN
• TANGO
• System Identification Toolbox
• BoolFilter

Cites Work

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• Efficient Likelihood Evaluation of State-Space Representations
• Monte Carlo Smoothing for Nonlinear Time Series
• New developments in state estimation for nonlinear systems

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