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This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) edited by S.
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Summary statistics and sequential methods for approximate Bayesian computation
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An overview of composite likelihood methods
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A novel approach for choosing summary statistics in approximate Bayesian computation
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Considerate approaches to constructing summary statistics for ABC model selection
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Estimation of demo-genetic model probabilities with approximate Bayesian computation using linear discriminant analysis on summary statistics
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Constructing summary statistics for approximate Bayesian computation: Semi-automatic ABC
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Contribution to the discussion of Fearnhead and Prangle (2012)
Filippi, S., C. P. Barnes, and M. P. H. Stumpf (2012) · 2012
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Semi-automatic selection of summary statistics for ABC model choice
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ABC model choice via random forests
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Approximate Bayesian Computation with proper scoring rules
Ruli, E., N. Sartori, and L. Ventura (2014) · 2014
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Amount of information needed for model choice in approximate Bayesian computation
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Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields
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Parameter estimation in hidden Markov models with intractable likelihoods using sequential Monte Carlo
Yıldırım, S., S. Singh, T. Dean, and A. Jasra (2014) · 2014
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The rate of convergence for approximate Bayesian computation
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Bayesian indirect inference using a parametric auxiliary model
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Approximate Bayesian computation for a class of time series models
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Approximate Bayesian Computation with composite score functions
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