A survey of convergence results on particle filtering methods for practitioners

By D. Crisan, Arnaud Doucet. IEEE Transactions on Signal Processing, 2002

1,071 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0213e3968e7b39812e56aa10bc2c0e8b085fdda9

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Citations in the corpus

Information, Divergence and Risk for Binary Experiments (2009, methodology): “Weighted empirical distributions are widely used in particle filtering (Crisan and Doucet, 2002).”
Multisensor data fusion: A review of the state-of-the-art (2013, methodology): “Probabilistic [32,40,45] Represents sensory data using probability”
Particle filters for partially observed diffusions (2007, methodology): “Our method replaces in the weight the intractable term μg with its unbiased estimator r.”
Approximate McKean–Vlasov representations for a class of SPDEs (2005, methodology): “In filtering theory, the generic name for such methods is that of a particle filter (see [4], [5], [6], [9], etc.”
Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters (2010, result): “This approximates over intervals in , with convergence results given in [31].”
A Review of Data Fusion Techniques (2013, methodology): “Particle filters are recursive implementations of the sequential Monte Carlo methods [39].”
SMC2: an efficient algorithm for sequential analysis of state space models (2011, methodology): “Fearnhead et al. (2010a). The connection with the auxiliary representation underlying PMCMC is pivotal for designing the MCMC rejuvenation steps, which are crucial for the success of IBIS.”
Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms (2006, background): “This leads to a crucial property: SIS with resampling can produce reasonable statistics only for systems that “exponentially forget” their past [8] (i.”
Multimodal fusion for multimedia analysis: a survey (2010, methodology): “These methods are also known as Sequential Monte Carlo (SMC) methods [33].”
Particle filter theory and practice with positioning applications (2010, background): “Further, most convergence results as surveyed in [56] are restricted to bounded functions of the state g(x) such that|g(x)| < C for someC.”
An overview of sequential Monte Carlo methods for parameter estimation in general state-space models (2009, background): “Convergence results Many sharp convergence results are available for SMC algorithms; see [ 12 ] for a survey for practitioners and [14] for a book length review.”
A Random-Finite-Set Approach to Bayesian SLAM (2011, background): “It is well known that SMC techniques are more amenable to expectation operations than maximization operations, since if p is approximated by a set of weighted samples {w(i) ,X}i=1 , then [26], [27]”
Convolution Particle Filter for Parameter Estimation in General State-Space Models (2009, background): “The particle filters (PFs) propose a good alternative: in many practical cases they give better results, moreover their theoretical properties are becoming increasingly better understood [7, 10, 6].”
Using Random Quasi-Monte-Carlo Within Particle Filters, With Application to Financial Time Series (2005, background): “Particle (cid:12)lters have good theoretical properties (see Crisan and Doucet, 2002, and references therein), and under mild regularity conditions, the rate of convergence of the particle approximation to the true posterior is O ( N (cid:0) 1 = 2 ) where N is the number of particles.”