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A tutorial on particle filtering and smoothing: fifteen years later
 OXFORD HANDBOOK OF NONLINEAR FILTERING
, 2011
"... Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. r ..."
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Cited by 206 (15 self)
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Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete, uptodate survey of this field as of 2008. Basic and advanced particle methods for filtering as well as smoothing are presented.
Fast Particle Smoothing: If I Had a Million Particles
 In International Conference on Machine Learning (ICML
, 2006
"... We propose e#cient particle smoothing methods for generalized statespaces models. ..."
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Cited by 51 (7 self)
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We propose e#cient particle smoothing methods for generalized statespaces models.
An Overview of Sequential Monte Carlo Methods for Parameter Estimation
 in General StateSpace Models,” in IFAC System Identification, no. Ml
, 2009
"... Abstract: Nonlinear nonGaussian statespace models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many ..."
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Cited by 48 (7 self)
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Abstract: Nonlinear nonGaussian statespace models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the statespace model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general statespace models. We discuss the advantages and limitations of these methods.
System Identification of Nonlinear StateSpace Models
, 2009
"... This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient i ..."
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Cited by 39 (18 self)
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This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient is the employment of socalled “particle smoothing” methods to compute required conditional expectations via a sequential Monte Carlo approach. Simulation examples demonstrate the efficacy of these techniques.
A sequential smoothing algorithm with linear computational cost
, 2008
"... In this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N 2) computational cost of most smoothers and will result in faster rates of convergence for fixed computational cost. The new method ..."
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Cited by 34 (2 self)
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In this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N 2) computational cost of most smoothers and will result in faster rates of convergence for fixed computational cost. The new method also overcomes some of the degeneracy problems we identify in many existing algorithms. Through simulation studies we show that substantial gains in efficiency are obtained for practical amounts of computational cost. It is shown both through these simulation studies, and on the analysis of an athletics data set, that our new method also substantially outperforms the simple FilterSmoother (the only other smoother with computational cost that is linear in the number of particles). 1
Sequential Monte Carlo smoothing for general state space Hidden Markov Models
 Ann. Appl. Probab
, 2011
"... ar ..."
M.: Expectation propagation for inference in nonlinear dynamical models with Poisson observations
 In: Proc IEEE Nonlinear Statistical Signal Processing Workshop. (2006
"... Neural activity unfolding over time can be modeled using nonlinear dynamical systems [1]. As neurons communicate via discrete action potentials, their activity can be characterized by the numbers of events occurring within short predefined timebins (spike counts). Because the observed data are hig ..."
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Cited by 9 (2 self)
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Neural activity unfolding over time can be modeled using nonlinear dynamical systems [1]. As neurons communicate via discrete action potentials, their activity can be characterized by the numbers of events occurring within short predefined timebins (spike counts). Because the observed data are highdimensional vectors of nonnegative integers, nonlinear state estimation from spike counts presents a unique set of challenges. In this paper, we describe why the expectation propagation (EP) framework is particularly wellsuited to this problem. We then demonstrate ways to improve the robustness and accuracy of Gaussian quadraturebased EP. Compared to the unscented Kalman smoother, we find that EPbased state estimators provide more accurate state estimates. 1.
Particle Filtering and Smoothing: Fifteen years Later, Handbook of Nonlinear Filtering
"... Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit finitedimensional solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online mann ..."
Abstract

Cited by 7 (0 self)
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Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit finitedimensional solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner (that is, recursively, as observations become available), and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete, uptodate survey of this field as of 2008. Basic and advanced particle methods for filtering as well as smoothing are presented.
2014): “On Particle Methods for Parameter Estimation in StateSpace Models,” arXiv Working Paper
"... Abstract. Nonlinear nonGaussian statespace models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. ..."
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Cited by 6 (1 self)
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Abstract. Nonlinear nonGaussian statespace models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. However, in most applications, the statespace model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard particle methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive review of particle methods that have been proposed to perform static parameter estimation in statespace models. We discuss the advantages and limitations of these methods and illustrate their performance on simple models. Key words and phrases: Bayesian inference, maximum likelihood inference, particle filtering, Sequential Monte Carlo, statespace models. 1.