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Exact filtering for partially-oberved continuous-time models.

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Exact filtering for partially-oberved continuous-time models. / Fearnhead, Paul; Meligkotsidou, Loukia.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 66, No. 3, 08.2004, p. 771-789.

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

Harvard

Fearnhead, P & Meligkotsidou, L 2004, 'Exact filtering for partially-oberved continuous-time models.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 66, no. 3, pp. 771-789. https://doi.org/10.1111/j.1467-9868.2004.05561.x

APA

Fearnhead, P., & Meligkotsidou, L. (2004). Exact filtering for partially-oberved continuous-time models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(3), 771-789. https://doi.org/10.1111/j.1467-9868.2004.05561.x

Vancouver

Fearnhead P, Meligkotsidou L. Exact filtering for partially-oberved continuous-time models. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 Aug;66(3):771-789. doi: 10.1111/j.1467-9868.2004.05561.x

Author

Fearnhead, Paul ; Meligkotsidou, Loukia. / Exact filtering for partially-oberved continuous-time models. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 ; Vol. 66, No. 3. pp. 771-789.

Bibtex

@article{345baba1ae9149809abba7ace7c7f564,
title = "Exact filtering for partially-oberved continuous-time models.",
abstract = "Summary. The forward–backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, we show how the forward–backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of a specific event in a Markov process, as this time is the sum of exponentially distributed interevent times. This enables us to apply the forward–backward algorithm to a range of new problems. We demonstrate our method on three problems: calculating likelihoods and simulating allele frequencies under a non-neutral population genetic model, analysing a stochastic epidemic model and simulating speciation times in phylogenetics.",
author = "Paul Fearnhead and Loukia Meligkotsidou",
year = "2004",
month = aug,
doi = "10.1111/j.1467-9868.2004.05561.x",
language = "English",
volume = "66",
pages = "771--789",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Exact filtering for partially-oberved continuous-time models.

AU - Fearnhead, Paul

AU - Meligkotsidou, Loukia

PY - 2004/8

Y1 - 2004/8

N2 - Summary. The forward–backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, we show how the forward–backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of a specific event in a Markov process, as this time is the sum of exponentially distributed interevent times. This enables us to apply the forward–backward algorithm to a range of new problems. We demonstrate our method on three problems: calculating likelihoods and simulating allele frequencies under a non-neutral population genetic model, analysing a stochastic epidemic model and simulating speciation times in phylogenetics.

AB - Summary. The forward–backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, we show how the forward–backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of a specific event in a Markov process, as this time is the sum of exponentially distributed interevent times. This enables us to apply the forward–backward algorithm to a range of new problems. We demonstrate our method on three problems: calculating likelihoods and simulating allele frequencies under a non-neutral population genetic model, analysing a stochastic epidemic model and simulating speciation times in phylogenetics.

U2 - 10.1111/j.1467-9868.2004.05561.x

DO - 10.1111/j.1467-9868.2004.05561.x

M3 - Journal article

VL - 66

SP - 771

EP - 789

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 - 3

ER -