Mixture Kalman filters

By Rong Chen, Jun S. Liu. 2000

676 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods, Signal Processing

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Citations in the corpus

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015, methodology): “Hence, for a conditionally linear Gaussian model, the RBPF takes the form of a Mixture Kalman filter; see Chen and Liu (2000). Analogously to a standard PF, the SKH procedure allows us to to compute an empirical point-mass approximation of p(x1:t|y1:t) by keeping track of the complete history of the”
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images (2015, neutral): “, hidden Markov models and Kalman filters[33, 34, 35].”
Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference (2004, background): “No assumption is made on the model that generates the sequence of distributions of interest (πt), so that our theorem equally applies to those recent algorithms [Chopin (2002), Del Moral and Doucet (2002) and Cappé, Guillin, Marin and Robert (2004)] that have been developed for contexts that widely”
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs (2017, background): “The Rao–Blackwellized particle filter, also known as the marginalized particle filter, was designed to achieve precisely this [2, 22].”
A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography (2008, methodology): “A Gaussian mixture has been already used by Anderson and Anderson (1999) and Chen and Liu (2000) in the context of the nonlinear filter.”
Particle Filter SLAM with High Dimensional Vehicle Model (2009, methodology): “Furthermore, the Rao-Blackwellized particle filter (rbpf), also denoted the marginalized particle filter (mpf) [1, 5, 7, 14, 15, 32], enables estimation of velocity, acceleration, and sensor error models by utilizing any linear Gaussian substructure in the model, which is fundamental for performance”
Smoothing algorithms for state–space models (2010, methodology): “Using equation (53), it is possible to combine the approximation of the (generalised) backward information filter with an approximation based on a standard Rao-Blackwellised SMC filter presented in Chen and Liu (2000) and Doucet et al. (2000)”
Marginalized particle filters for mixed linear/nonlinear state-space models (2005, neutral): “, [2], [7], [8], [12], [14], and [31].”
Modified input estimation technique for tracking manoeuvring targets (2009, methodology): “Example 3: Since the performance of the proposed algorithm is presented as an estimation of the tracking of positions, velocities and accelerations, a comparison based on the Monte-Carlo simulation [28, 29] is also made to”
Bayesian Filtering and Smoothing (2013, background): “, 2004), which is also called the mixture Kalman filter (MKF) (Chen and Liu, 2000) is that sometimes it is possible to evaluate some of the filtering equations analytically and the others with Monte Carlo sampling instead of computing everything with pure sampling.”
A family of algorithms for approximate Bayesian inference (2001, background): “One is sequential Monte Carlo, a family of nondeterministic techniques (Liu & Chen, 2000; Carpenter et al., 1999).”
Skill and Luck in Private Equity Performance (2017, neutral): “See West (1992), Diebolt and Robert (1994), and Chen and Liu (2000) for more details on the estimation of mixture distributions.”
Convergence of Sequential Monte Carlo Methods (2007, neutral): “…to design a more efficient algorithm by focusing on the estimation of the lower-dimensional posterior distributions π t (da 0:t ) as, conditional upon (a t ), the model (15)-(16) is a standard linear Gaussian state-space model; see (Chen & Liu, 2000; Doucet, Godsill & Andrieu, 2000) for details.”