Approximate Bayesian computation using indirect inference

By C. Drovandi, Anthony N. Pettitt, Malcolm J. Faddy. 2011

109 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f38577ea5afa9f339fd5e36d4bc35b4bcc4b037a

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Citations in the corpus

A comparative review of dimension reduction methods in approximate Bayesian computation (2012, methodology): “Drovandi et al. (2011) proposed to adopt ideas from indirect inference (e.g. Heggland and Frigessi 2004; Jiang and Turnbull 2004) as a means to identify summary statistics for an ABC analysis.”
Statistical inference for stochastic simulation models--theory and application. (2011, methodology): “…the method of indirect inference uses as summary statistics the parameter estimates of a so called auxiliary , intermediate or indirect statistical model that is fit to simulated and observed data (Gourieroux et al. 1993; Heggland & Frigessi 2004; Jiang & Turnbull 2004; Drovandi et al. 2011).”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, methodology): “A second perspective on the ABC output is that it is based on a non-parametric approximation of the sampling distribution (Blum 2010; Blum and Fran¸cois 2010), connected with both indirect inference (Drovandi et al. 2011) and k - nearest neighbour estimation (Biau et al. 2014).”
A Review of Modern Computational Algorithms for Bayesian Optimal Design (2016, methodology): “ABC has commonly been used to perform inference (e.g. Drovandi and Pettitt, 2011; Drovandi et al., 2011; Sisson and Fan, 2011).”
Bayesian computation: a summary of the current state, and samples backwards and forwards (2015, methodology): “A second perspective on the ABC output is that it is based on a non-parametric approximation of the sampling distribution (Blum 2010; Blum and François 2010), connected with both indirect inference (Drovandi et al. 2011) and k -nearest neighbour estimation (Biau et al. 2014).”
Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation (2012, methodology): “It is also the idea behind the approach of Drovandi et al. (2011), who summarized the data through parameter estimates under an approximating model.”
Asymptotic properties of approximate Bayesian computation (2016, background): “With reference to the rates in equation (9), consider, for simplicity’s sake, the case where dT (1) = · · · = dT (k) = dT and the distances are Euclidean, with k ≥ dθ.”
Likelihood-free inference via classification (2014, methodology): “The latter methods are traditionally used in a classical inference framework while ABC has its roots in Bayesian inference, but the boundaries have started to blur (Drovandi et al. 2011).”
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference (2015, unknown): “Connections between ABC and indirect inference have been made previously by [7] as a novel way of creating summary statistics.”