An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

Cappé, Olivier; Godsill, Simon; Moulines, Éric

doi:10.1109/jproc.2007.893250

Cited by 824 publications

(606 citation statements)

References 86 publications

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“…In this paper, a robust graph matching algorithm based on the Sequential Monte Carlo (SMC) framework [13] is proposed. Unlike previous methods, the algorithm sequentially samples potential matches via proposal distributions to maximize the graph matching objective in a robust and effective manner.…”

Section: Introductionmentioning

confidence: 99%

Graph Matching via Sequential Monte Carlo

Suh

Cho

Lee

2012

Computer Vision – ECCV 2012

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Abstract. Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers.

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Section: Introductionmentioning

confidence: 99%

Graph Matching via Sequential Monte Carlo

Suh

Cho

Lee

2012

Computer Vision – ECCV 2012

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“…The classification methods were used with their default parameters. A sensitivity analysis was conducted on the parameter setting of each one of the regression methods employed using a simplified version of the sequential Monte Carlo method [8].…”

Section: Experiments and Resultsmentioning

confidence: 99%

On Predicting a Call Center’s Workload: A Discretization-Based Approach

Moreira-Matias

Nunes

Ferreira

et al. 2014

Lecture Notes in Computer Science

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Abstract. Agent scheduling in call centers is a major management problem as the optimal ratio between service quality and costs is hardly achieved. In the literature, regression and time series analysis methods have been used to address this problem by predicting the future arrival counts. In this paper, we propose to discretize these target variables into finite intervals. By reducing its domain length, the goal is to accurately mine the demand peaks as these are the main cause for abandoned calls. This was done by employing multi-class classification. This approach was tested on a real-world dataset acquired through a taxi dispatching call center. The results demonstrate that this framework can accurately reduce the number of abandoned calls, while maintaining a reasonable staff-based cost.

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“…* Under usual conditions where h k is a tractable probability density, the particle filtration 8,29 of the nonlinear states ξ k is achieved by the Bayesian update in the form…”

Section: Particle Filteringmentioning

confidence: 99%

“…First, if they are linear (or mildly nonlinear) functions, the celebrated Kalman filter (or its extended or unscented variants) dominates the class of possible solutions. [5][6][7] Second, if f k is linear or nonlinear and h k severely nonlinear but known and computationally tractable, the particle filters estimating x k and ξ k via a Monte Carlo sampling from the state space prevail, eg, the work of Cappé et al 8 Moreover, the model structure allows marginalized (Rao-Blackwellized) particle filtering (MPF): ξ k is sampled, whereas x k is estimated via a Kalman filter. 9,10 The marginalization reduces the number of necessary particles and leads to a lower estimator variance.…”

Section: Introductionmentioning

confidence: 99%

Marginalized approximate filtering of state‐space models

Dedecius

2017

Adaptive Control & Signal

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SummaryThe marginalized particle filtering (MPF) is a powerful technique reducing the number of particles necessary to effectively estimate hidden states of state-space models. This paper alleviates the assumption of a fully known and computationally tractable observation model. Exploiting the recent developments in the theory of approximate Bayesian computation (ABC) filtration, an ABC counterpart of MPF is proposed, applicable when the observation model is too complex to be evaluated analytically or even numerically, but it is still possible to sample from it by plugging in the state. The novelty is 2-fold. First, ABC methods have not been used in marginalized filtering yet. Second, a new multivariate robust method for evaluation of particle weights is proposed. The goal of this paper is to demonstrate the idea on the background of the MPF with a particular accent on exposition.

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An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

Cited by 824 publications

References 86 publications

Graph Matching via Sequential Monte Carlo

Graph Matching via Sequential Monte Carlo

On Predicting a Call Center’s Workload: A Discretization-Based Approach

Marginalized approximate filtering of state‐space models

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