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Taking error into account when fitting models using approximate Bayesian computation

van der Vaart, E., Prangle, D. and Sibly, R. M. ORCID: (2018) Taking error into account when fitting models using approximate Bayesian computation. Ecological Applications, 28 (2). pp. 267-274. ISSN 0051-0761

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To link to this item DOI: 10.1002/eap.1656


Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the 'coverage test' with which accuracy is assessed. We apply this method - which we call 'error-calibrated ABC' - to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic 'coverage test' show that our approach improves estimation of parameter values and their credible intervals for both models.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
ID Code:74330
Uncontrolled Keywords:ABC, IBM, approximate Bayesian computation, individual-based model, parameter estimation
Publisher:Ecological Society of America


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