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We analyze the behavior of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data generating process; i.e., when the data simulator in ABC is misspecified.
Markov chain Monte Carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., and Tavare, S. (2003) · 2003
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Sisson, S. A., Fan, Y., and Tanaka, M. M. (2007) · 2007
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Non-linear regression models for approximate bayesian computation
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Likelihood-free bayesian estimation of multivariate quantile distributions
Drovandi, C. C. and Pettitt, A. N. (2011) · 2011
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abc: an r package for approximate Bayesian computation (ABC)
Csilléry, K., François, O., and Blum, M. G. (2012) · 2012
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The Bernstein-von-Mises theorem under misspecification
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Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
Regression approaches for ABC
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Asymptotic properties of approximate Bayesian computation
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