ArticlePLoS computational biology2013
Approximate Bayesian computation.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 217 papers, 1 of them a synthesis that pooled it.
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Who cites it
217 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025Pooled it
- Bayesian inference of physiologically meaningful parameters from body sway measurements.Scientific reports · 2017Trial
- Genomic insights into natural selection in recent human history.Nature reviews. Genetics · 2026Review
- Forecasting the future of American mass shootings with Bayesian agent-based model calibration.PNAS nexus · 2026Article
- The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size.PLoS computational biology · 2026Article
- An agent-based model of Trypanosoma brucei social motility to explore determinants of colony pattern formation.PLoS computational biology · 2026Article
- Simulation and inference methods for non-Markovian stochastic reaction networks.PLoS computational biology · 2026Article
- Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia.Nature communications · 2026Article
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Molecular kinetics dictate population dynamics in CRISPR-based plasmid defense.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Developing and Calibrating a Colorectal Cancer Microsimulation Model for Northern Ireland.PharmacoEconomics · 2026Article
- A virtual cohort framework with applications to adoptive cell therapy in bladder cancer.bioRxiv : the preprint server for biology · 2026Article
- Anatomically aware simulation of patient-specific glioblastoma xenografts.PLoS computational biology · 2026Article
- Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.PloS one · 2026Article
- Article
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Review
- Studying COVID-19 transmission in US state prisons using an agent-based modelling approach: a simulation study.BMJ open · 2025Article
- Planning for the sustainability of a youth suicide prevention program in Native American contexts: A modeling study.Annals of epidemiology · 2025Article
- Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration.PLoS computational biology · 2025Article
157 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).
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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.