Stochastic Models, Estimation And Control

By P. Maybeck. 2012

1,981 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Finance

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Citations in the corpus

Variational Continual Learning (2017, neutral): “…projection operators previously identiﬁed, to i) Laplace propagation (Smola et al., 2004), ii) online VI (Ghahramani and Attias, 2000; Sato, 2001) also known as streaming VB (Broderick et al., 2013), iii) assumed density ﬁltering (Maybeck, 1982) and iv) sequential Monte Carlo (Liu and Chen, 1998).”
A Provably Convergent Multifidelity Optimization Algorithm not Requiring High-Fidelity Derivatives (2010, methodology): “A thorough discussion of using a maximum likelihood estimator to combine two or more estimates with normally distributed uncertainties is available in [[35], Chapter 1].”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, background): “For nonlinear problems, smoothing approaches are generally more accurate than filtering, since they relinearize past measurements [25].”
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, background): “An additional important feature of stochastic estimation algorithms like the EKFs is that gyro drift bias can be estimated by state vector augmentation techniques [57].”
A framework for maximum likelihood parameter identification applied on MAVs (2018, neutral): “Fv,k = ∂rM,k(xk+1, xk,θ,wk) ∂wk (x̄k+1, x̄k, ?̄?,0) (29)”
Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives (2012, methodology): “A thorough discussion of using a maximum likelihood estimator to combine two or more estimates with normally distributed uncertainties is available in [[35], Chapter 1].”
Applied Stochastic Differential Equations (2019, positive): “7 Approximate Continuous-Discrete Filtering For nonlinear filtering problems, there exists various approximations (see, e.g., Jazwinski, 1970; Maybeck, 1982a; Särkkä, 2006, 2013).”
Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters (2010, background): “The linear, quadratic cost, Gaussian distribution (LQG) assumptions that lead to the certainty equivalence principle, separating the estimation and control problems, are typically not valid in this problem [1].”
See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks (2011, methodology): “For information on the derivation of this algorithm, there are many textbooks on the topic of Kalman filtering [26], [27].”
A Survey of Fault Detection, Isolation, and Reconfiguration Methods (2010, methodology): “A well-known Kalman filter-based approach is the multiple model adaptive estimation (MMAE) approach [65]–[67].”