Filtering via approximate Bayesian computation

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Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, methodology): “model itself is given and sampling is possible, but computation of its values is expensive or even impossible. Therefore ordinary Bayes’ rule cannot be used for ﬁltering. To overcome this limitation, Jasra et al. (2012) and Calvet and Czellar (2015) proposed to apply approximate Bayesian 5 computati”
Approximate Bayesian computational methods (2011, methodology): “It is worth pointing out the remark made by Leuenberger et al. (2010) about model choice and the use of the approximation of the normalising constant resulting from the modelling to get to the marginal likelihood and the computation of the Bayes factor.”
Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions (2014, methodology): “In relation to this, we then investigate the SMC simulation of a filter based upon Approximate Bayesian Computation (ABC) [20].”
Accurate Methods for Approximate Bayesian Computation Filtering (2015, methodology): “An alternative, proposed by Jasra et al. (2011), is to use a ﬁlter based on Ap-proximate Bayesian Computation.”