ABCtoolbox was designed to perform Approximate Bayesian Computation (ABC) estimations using various recently published algorithms including MCMC without likelihood and Population Monte Carlo. Due to its potential to interact with almost any command line simulation software, ABCtoolbox can be used to study problems in different areas including genomics or population genetics.

References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Ariella L.Gladstein; Consuelo D. Quinto-Cortés; Julian L. Pistorius; David Christy; Logan Gantner; Blake L. Joyce: SimPrily: A Python framework to simplify high-throughput genomic simulations (2018) not zbMATH
  2. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  3. Rodrigues, G. S.; Prangle, D.; Sisson, S. A.: Recalibration: a post-processing method for approximate Bayesian computation (2018)
  4. Prangle, D.; Blum, M. G. B.; Popovic, G.; Sisson, S. A.: Diagnostic tools for approximate Bayesian computation using the coverage property (2014)
  5. Lenormand, Maxime; Jabot, Franck; Deffuant, Guillaume: Adaptive approximate Bayesian computation for complex models (2013)
  6. Ozcaglar, Cagri; Shabbeer, Amina; Vandenberg, Scott L.; Yener, Bülent; Bennett, Kristin P.: Epidemiological models of Mycobacterium tuberculosis complex infections (2012)
  7. Huang, Wen; Takebayashi, Naoki; Qi, Yan; Hickerson, Michael J.: MTML-msbayes: approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity (2011) ioport
  8. Netto, Marco A. S.; Vecchiola, Christian; Kirley, Michael; Varela, Carlos A.; Buyya, Rajkumar: Use of run time predictions for automatic co-allocation of multi-cluster resources for iterative parallel applications (2011) ioport
  9. Wegmann, Daniel; Leuenberger, Christoph; Neuenschwander, Samuel; Excoffier, Laurent: Abctoolbox: a versatile toolkit for approximate Bayesian computations (2010) ioport