Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference

Wu, Jiangqi; Wen, Linjie; Green, Peter L.; Li, Jinglai; Maskell, Simon

doi:10.1007/s11222-021-10075-x

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…It would also be interesting to incorporate ideas from CW-IEKI into the method of Wu et al (2022), which uses EKI as the forward kernel in data annealing SMC. This approach is exact, and requires much fewer evaluations of G(•) than SMC with a Metropolis-Hastings forward kernel, but it currently requires the covariance of the likelihood function to be known.…”

Section: Discussionmentioning

confidence: 99%

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

Botha,

Adams,

Frazier

et al. 2023

Inverse Problems

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The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed for parameter inference of static Bayesian models with a Gaussian likelihood, in a way that is analogous to likelihood tempering sequential Monte Carlo (SMC). These methods are commonly referred to as ensemble Kalman inversion (EKI). Unlike SMC, the inference from EKI is asymptotically biased if the likelihood is non-linear and\slash or non-Gaussian and if the priors are non-Gaussian. However, it is significantly faster to run. Currently, a large limitation of EKI methods is that the covariance of the measurement error is assumed to be fully known. We develop a new method, which we call component-wise iterative ensemble Kalman inversion (CW-IEKI), that allows elements of the covariance matrix to be inferred alongside the model parameters at negligible extra cost. This novel method is compared to SMC on a linear Gaussian example as well as four examples with non-linear dynamics (i.e. non-linear function of the model parameters). The non-linear examples include a set of population models applied to synthetic data, a model of nitrogen mineralisation in soil that is based on the Agricultural Production Systems Simulator (APSIM), a model predicting seagrass decline due to stress from water temperature and light, and a model predicting coral calcification rates. On our examples, we find that CW-IEKI has relatively similar predictive performance to SMC, albeit with greater uncertainty, and it has a significantly faster run time.

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Section: Discussionmentioning

confidence: 99%

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

Botha,

Adams,

Frazier

et al. 2023

Inverse Problems

View full text Add to dashboard Cite

show abstract

“…It would also be interesting to incorporate the CW-IEKI method into the data annealing SMC algorithm of Wu et al (2022), which currently requires the covariance of the likelihood function to be known. In particular, one extension here is developing a likelihood tempering SMC algorithm with our CW-IEKI method as the forward kernel.…”

Section: Discussionmentioning

confidence: 99%

“…The ensemble Kalman sampler (Garbuno-Inigo et al, 2020;Ding and Li, 2021) is a variation of EKI, which perturbs the ensemble instead of the observations as in regular EKI. Duffield and Singh (2021) extend EKI for non-Gaussian likelihoods through a Gaussian approximation of the likelihood, and Wu et al (2022) use EKI as the forward kernel in data annealing SMC. The latter method is exact, and requires much fewer evaluations of G(•) than SMC with a Metropolis-Hastings forward kernel.…”

Section: Introductionmentioning

confidence: 99%

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

Botha¹,

Adams²,

Tran³

et al. 2022

Preprint

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The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed for parameter inference of static Bayesian models with a Gaussian likelihood, in a way that is analogous to likelihood tempering sequential Monte Carlo (SMC). These methods are commonly referred to as ensemble Kalman inversion (EKI). Unlike SMC, the inference from EKI is only asymptotically unbiased if the likelihood is linear Gaussian and the priors are Gaussian. However, EKI is significantly faster to run. Currently, a large limitation of EKI methods is that the covariance of the measurement error is assumed to be fully known. We develop a new method, which we call component-wise iterative ensemble Kalman inversion (CW-IEKI), that allows elements of the covariance matrix to be inferred alongside the model parameters at negligible extra cost. This novel method is compared to SMC on three different application examples: a model of nitrogen mineralisation in soil that is based on the Agricultural Production Systems Simulator (APSIM), a model predicting seagrass decline due to stress from water temperature and light, and a model predicting coral calcification rates. On all of these examples, we find that CW-IEKI has relatively similar predictive performance to SMC, albeit with greater uncertainty, and it has a significantly faster run time.

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“…Combining SAR and optical imagery information with the groundbreaking statistical samplers (Corenflos et al, 2021;Wu et al, 2022;Hao et al, 2023a;Hao et al, 2023b) for tracking approaches will make analysing ocean currents, and wave structures possible, which can lead to developing back-tracking approaches to predict the sources of plastic pollution. Lastly, In order to mitigate the drawbacks of small-sized plastic bits and relatively low spatial resolution remote sensing data, group (or, namely, cloud) tracking-based approaches (Mihaylova et al, 2014) are of great importance.…”

Section: Tracking the Source Of Pollutionmentioning

confidence: 99%

On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring

Karakuş

2023

Front. Remote Sens.

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Marine plastic pollution is an emerging environmental problem since it pollutes the ocean, air and food whilst endangering the ocean wildlife via the ingestion and entanglements. During the last decade, an enormous effort has been spent on finding possible solutions to marine plastic pollution. Remote sensing imagery sits in a crucial place for these efforts since it provides informative earth observation products, and the current technology offers further essential development. Despite the advances in the last decade, there is still a way to go for marine plastic monitoring research where challenges are rarely highlighted. This paper contributes to the literature with a critical review and aims to highlight literature milestones in marine debris and suspected plastics (MD&SP) monitoring by promoting the computational imaging methodology behind these approaches along with detailed discussions on challenges and potential future research directions.

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Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference

Cited by 6 publications

References 34 publications

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring

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