ArticleStatistics and computing2018
Likelihood-free inference via classification.
Article in Statistics and computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 88 citations in OpenAlex.
- TraitTrainR: accelerating large-scale simulation under models of continuous trait evolution.Bioinformatics advances · 2025Article
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- Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.PLoS computational biology · 2022Article
- Scalable machine learning-assisted model exploration and inference using Sciope.Bioinformatics (Oxford, England) · 2021Article
- Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.eLife · 2021Article
- The frontier of simulation-based inference.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Mining gold from implicit models to improve likelihood-free inference.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- An automatic adaptive method to combine summary statistics in approximate Bayesian computation.PloS one · 2020Article
- Classifying evolutionary forces in language change using neural networks.Evolutionary human sciences · 2020Article
- Bayesian inference of spreading processes on networks.Proceedings. Mathematical, physical, and engineering sciences · 2018Article
- Likelihood-free inference via classification.Statistics and computing · 2018Article
- Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation With High Performance Computing.Frontiers in physiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.