Random weight particle filtering of continuous-time processes.

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Random weight particle filtering of continuous-time processes. / Fearnhead, Paul ; Papaspiliopoulos, Omiros; Roberts, Gareth O. et al.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 72, No. 4, 09.2010, p. 497-512.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fearnhead, P , Papaspiliopoulos, O, Roberts, GO & Stuart, A 2010, 'Random weight particle filtering of continuous-time processes.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 72, no. 4, pp. 497-512. https://doi.org/10.1111/j.1467-9868.2010.00744.x

APA

Fearnhead, P., Papaspiliopoulos, O., Roberts, G. O., & Stuart, A. (2010). Random weight particle filtering of continuous-time processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 497-512. https://doi.org/10.1111/j.1467-9868.2010.00744.x

Vancouver

Fearnhead P , Papaspiliopoulos O, Roberts GO, Stuart A. Random weight particle filtering of continuous-time processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2010 Sept;72(4):497-512. doi: 10.1111/j.1467-9868.2010.00744.x

Author

Fearnhead, Paul ; Papaspiliopoulos, Omiros ; Roberts, Gareth O. et al. / Random weight particle filtering of continuous-time processes. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2010 ; Vol. 72, No. 4. pp. 497-512.

Bibtex

@article{50f5c4459ff74454b22966674cafef89,

title = "Random weight particle filtering of continuous-time processes.",

abstract = "It is possible to implement importance sampling, and particle filter algorithms, where the importance sampling weight is random. Such random-weight algorithms have been shown to be efficient for inference for a class of diffusion models, as they enable inference without any (time discretization) approximation of the underlying diffusion model. One difficulty of implementing such random-weight algorithms is the requirement to have weights that are positive with probability 1. We show how Wald's identity for martingales can be used to ensure positive weights. We apply this idea to analysis of diffusion models from high frequency data. For a class of diffusion models we show how to implement a particle filter, which uses all the information in the data, but whose computational cost is independent of the frequency of the data. We use the Wald identity to implement a random-weight particle filter for these models which avoids time discretization error.",

keywords = "* Diffusions, * Exact simulation, * Gaussian process, * Integrated processes, * Negative importance weights, * Poisson estimator, * Sequential Monte Carlo methods",

author = "Paul Fearnhead and Omiros Papaspiliopoulos and Roberts, {Gareth O.} and Andrew Stuart",

year = "2010",

month = sep,

doi = "10.1111/j.1467-9868.2010.00744.x",

language = "English",

volume = "72",

pages = "497--512",

journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",

issn = "1369-7412",

publisher = "Wiley-Blackwell",

number = "4",

}

RIS

TY - JOUR

T1 - Random weight particle filtering of continuous-time processes.

AU - Fearnhead, Paul

AU - Papaspiliopoulos, Omiros

AU - Roberts, Gareth O.

AU - Stuart, Andrew

PY - 2010/9

Y1 - 2010/9

N2 - It is possible to implement importance sampling, and particle filter algorithms, where the importance sampling weight is random. Such random-weight algorithms have been shown to be efficient for inference for a class of diffusion models, as they enable inference without any (time discretization) approximation of the underlying diffusion model. One difficulty of implementing such random-weight algorithms is the requirement to have weights that are positive with probability 1. We show how Wald's identity for martingales can be used to ensure positive weights. We apply this idea to analysis of diffusion models from high frequency data. For a class of diffusion models we show how to implement a particle filter, which uses all the information in the data, but whose computational cost is independent of the frequency of the data. We use the Wald identity to implement a random-weight particle filter for these models which avoids time discretization error.

AB - It is possible to implement importance sampling, and particle filter algorithms, where the importance sampling weight is random. Such random-weight algorithms have been shown to be efficient for inference for a class of diffusion models, as they enable inference without any (time discretization) approximation of the underlying diffusion model. One difficulty of implementing such random-weight algorithms is the requirement to have weights that are positive with probability 1. We show how Wald's identity for martingales can be used to ensure positive weights. We apply this idea to analysis of diffusion models from high frequency data. For a class of diffusion models we show how to implement a particle filter, which uses all the information in the data, but whose computational cost is independent of the frequency of the data. We use the Wald identity to implement a random-weight particle filter for these models which avoids time discretization error.

KW - Diffusions

KW - Exact simulation

KW - Gaussian process

KW - Integrated processes

KW - Negative importance weights

KW - Poisson estimator

KW - Sequential Monte Carlo methods

U2 - 10.1111/j.1467-9868.2010.00744.x

DO - 10.1111/j.1467-9868.2010.00744.x

M3 - Journal article

VL - 72

SP - 497

EP - 512

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 4

ER -

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