abc

abc: Tools for Approximate Bayesian Computation (ABC). Implements several ABC algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.

Keywords for this software

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References in zbMATH (referenced in 26 articles )

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  1. Kobayashi, Genya; Kakamu, Kazuhiko: Approximate Bayesian computation for Lorenz curves from grouped data (2019)
  2. Ziwen An, Leah F. South, Christopher C. Drovand: BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood (2019) arXiv
  3. Ho, Lam Si Tung; Crawford, Forrest W.; Suchard, Marc A.: Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease (2018)
  4. Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W.; Minin, Vladimir N.; Suchard, Marc A.: Birth/birth-death processes and their computable transition probabilities with biological applications (2018)
  5. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  6. Lambert, Ben; MacLean, Adam L.; Fletcher, Alexander G.; Combes, Alexander N.; Little, Melissa H.; Byrne, Helen M.: Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis (2018)
  7. Lintusaari, Jarno; Vuollekoski, Henri; Kangasrääsiö, Antti; Skytén, Kusti; Järvenpää, Marko; Marttinen, Pekka; Gutmann, Michael U.; Vehtari, Aki; Corander, Jukka; Kaski, Samuel: ELFI: engine for likelihood-free inference (2018)
  8. McKinley, Trevelyan J.; Vernon, Ian; Andrianakis, Ioannis; McCreesh, Nicky; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G.: Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models (2018)
  9. Nott, David J.; Drovandi, Christopher C.; Mengersen, Kerrie; Evans, Michael: Approximation of Bayesian predictive (p)-values with regression ABC (2018)
  10. Spantini, Alessio; Bigoni, Daniele; Marzouk, Youssef: Inference via low-dimensional couplings (2018)
  11. Dennis Prangle: gk: An R Package for the g-and-k and generalised g-and-h Distributions (2017) arXiv
  12. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  13. Guha, Nilabja; Tan, Xiaosi: Multilevel approximate Bayesian approaches for flows in highly heterogeneous porous media and their applications (2017)
  14. Hoitzing, Hanne; Johnston, Iain G.; Jones, Nick S.: Stochastic models for evolving cellular populations of mitochondria: disease, development, and ageing (2017)
  15. Owhadi, Houman; Scovel, Clint: Qualitative robustness in Bayesian inference (2017)
  16. Duforet-Frebourg, Nicolas; Slatkin, Montgomery: Isolation-by-distance-and-time in a stepping-stone model (2016)
  17. Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang: Bayesian analysis of rare events (2016)
  18. Bonassi, Fernando V.; West, Mike: Sequential Monte Carlo with adaptive weights for approximate Bayesian computation (2015)
  19. Brand, Samuel P. C.; Tildesley, Michael J.; Keeling, Matthew J.: Rapid simulation of spatial epidemics: a spectral method (2015)
  20. Holmes, William R.: A practical guide to the probability density approximation (PDA) with improved implementation and error characterization (2015)

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