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Journal of Statistical Computation and Simulation Volume 86, 2016 - Issue 1
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Original Articles

Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation

Umberto PicchiniCentre for Mathematical Sciences, Lund University, Lund, SwedenCorrespondence[email protected]
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Julie Lyng FormanDepartment of Biostatistics, University of Copenhagen, Copenhagen, DenmarkView further author information
Pages 195-213 | Received 04 Aug 2014, Accepted 07 Dec 2014, Published online: 19 Jan 2015

Citations (9)

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Richard G. Everitt & Paulina A. Rowińska. (2021) Delayed Acceptance ABC-SMC. Journal of Computational and Graphical Statistics 30:1, pages 55-66.
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Articles from other publishers (8)

Xiaonan Li, Ping Ma, Zehua Wu, Tao Chao & Ming Yang. (2023) An efficient two‐stage algorithm for parameter identification of non‐linear state‐space models‐based on Gaussian process regression. IET Control Theory & Applications 17:8, pages 985-1001.
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Boxuan Chen, Yang Zeng, Hu Wang & Enying Li. (2021) Approximate Bayesian assisted inverse method for identification of parameters of variable stiffness composite laminates. Composite Structures 267, pages 113853.
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Thomas P. Prescott & Ruth E. Baker. (2021) Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling. SIAM/ASA Journal on Uncertainty Quantification 9:2, pages 788-817.
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Yang Li, Zhenhua Han, Quanlu Zhang, Zhenhua Li & Haisheng Tan. (2020) Automating Cloud Deployment for Deep Learning Inference of Real-time Online Services. Automating Cloud Deployment for Deep Learning Inference of Real-time Online Services.
Evelyn Buckwar, Massimiliano Tamborrino & Irene Tubikanec. (2019) Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs. Statistics and Computing 30:3, pages 627-648.
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Amani A. Alahmadi, Jennifer A. Flegg, Davis G. Cochrane, Christopher C. Drovandi & Jonathan M. Keith. (2020) A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models. Royal Society Open Science 7:3, pages 191315.
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Umberto Picchini & Adeline Samson. (2017) Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models. Computational Statistics 33:1, pages 179-212.
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Patrick L. McDermott, Christopher K. Wikle & Joshua Millspaugh. (2017) Hierarchical Nonlinear Spatio-temporal Agent-Based Models for Collective Animal Movement. Journal of Agricultural, Biological and Environmental Statistics 22:3, pages 294-312.
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