Statistical Applications in Genetics and Molecular Biology Approximately Sufficient Statistics and Bayesian Computation

By Paul Joyce, Paul Marjoram. 2011

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Citations in the corpus

K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings (2015, methodology): “Experiments on a simulated scenario and a real-world biological problem illustrate the effectiveness of the proposed algorithm.”
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network (2015, methodology): “measure of sufficiency (Joyce and Marjoram (2008)), entropy (Nunes and Balding (2010)), Akaike and Bayesian information criteria (Blum, Nunes,”
Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation (2012, methodology): “To choose appropriate values for m and l we fitted linear models with m ranging over a grid of values between 60 and 140, and l ranging between 1 and 4, and we used BIC (averaged across the models for the four parameter values) to choose an appropriate value for m and l. We then used the summary sta”
A comparative review of dimension reduction methods in approximate Bayesian computation (2012, methodology): “These involve the analysis of a coalescent model with recombination (Joyce and Marjoram 2008), an evaluation of the evolutionary fitness cost of mutation in drug-resistant tuberculosis (Luciani et al. 2009), and an assessment of the number and size-distribution of particle inclusions in the…”
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models (2015, background): “One difficulty is the assessment of the discrepancy between the observed and simulated data (Joyce and Marjoram, 2008; Wegmann et al., 2009; Nunes and Balding, 2010; Fearnhead and Prangle, 2012; Aeschbacher et al., 2012; Gutmann et al., 2014).”
Approximate Bayesian Computation (2013, methodology): “An algorithm has been proposed for identifying a representative subset of summary statistics, by iteratively assessing whether an additional statistic introduces a meaningful modification of the posterior [31].”
Approximate Bayesian Computation in Evolution and Ecology (2010, methodology): “Joyce & Marjoram (2008) suggested an approach for choosing summary statistics from among a large candidate set.”
Kernel approximate Bayesian computation in population genetic inferences (2012, methodology): “Consequently, recent progress in ABC algorithm development has focused on techniques for selecting more informative summary statistics (Joyce and Marjoram, 2008; Wegmann et al., 2009; Blum and Francois, 2010; Nunes and Balding, 2010; Fearnhead and Prangle, 2012).”
Approximate Bayesian computational methods (2011, background): “Assuming a large collection of summary statistics is available, Joyce and Marjoram (2008) consider the sequential inclusion of those statistics into the ABC target.”
Likelihood-free Markov chain Monte Carlo (2010, unknown): “See also Joyce and Marjoram (2008).”
Lack of confidence in approximate Bayesian computation model choice (2011, methodology): “While acknowledging the gain brought by ABC in handling Bayesian inference in complex models, and the existence of involved summary selection mechanisms (17, 18), we demonstrate here that the loss due to the ABC approximation may be arbitrary in the specific setting of Bayesian model choice via post”
Statistical inference for stochastic simulation models--theory and application. (2011, background): “Joyce & Marjoram (2008) and Fearnhead & Prangle (2010) weight statistics by their importance for the inference.”
A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation (2012, methodology): “Joyce and Marjoram ( 2008 ) proposed a sequential scheme based on the principle of approx - 120 imate sufficiency .”