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Taking error into account when fitting models using Approximate Bayesian Computation
Author(s) -
Vaart Elske,
Prangle Dennis,
Sibly Richard M.
Publication year - 2018
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.1656
Subject(s) - approximate bayesian computation , computation , computer science , probabilistic logic , bayesian probability , variety (cybernetics) , protocol (science) , algorithm , estimation theory , data mining , statistics , artificial intelligence , mathematics , medicine , alternative medicine , pathology , inference
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.