A comparative review of dimension reduction methods in approximate Bayesian computation
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Cites methods from "A comparative review of dimension r..."
...Most importantly, when using large feature vectors, ABC is susceptible to the curse of dimensionality [59] – much effort has therefore gone into dimensionality reduction and feature selection for ABC (reviewed in [89])....
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Cites methods from "A comparative review of dimension r..."
...Keywords: Approximate Bayesian computation, model selection, summary statistics, k-nearest neighbors, likelihood-free methods, bagging, random forests, bootstrap, subsampling, posterior predictive, error rate, sparsity, Harlequin ladybird, Bayesian model choice....
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...…avoids the computation of the likelihood function (hence the alternative name of likelihood-free methods, Ratmann et al., 2007) and it progressively turned into a much more generic form of approximation technique, with applications to other forms of complex data, as covered in the above reviews....
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"A comparative review of dimension r..." refers background in this paper
...Each can be expressed as the sum of the maximized log-likelihood that measures the fit of the model to the data, and a penalty for model complexity (Akaike 1974; Schwarz 1978)....
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