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 37 articles )

Showing results 1 to 20 of 37.
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  1. Jhwueng, Dwueng-Chwuan: Modeling rate of adaptive trait evolution using Cox-Ingersoll-Ross process: an approximate Bayesian computation approach (2020)
  2. Thong, David; Streftaris, George; Gibson, Gavin J.: Latent likelihood ratio tests for assessing spatial kernels in epidemic models (2020)
  3. Griswold, Cortland K.: An ancestral process with selection in an ecological community (2019)
  4. Ke, Yuqin; Tian, Tianhai: Approximate Bayesian computational methods for the inference of unknown parameters (2019)
  5. Kobayashi, Genya; Kakamu, Kazuhiko: Approximate Bayesian computation for Lorenz curves from grouped data (2019)
  6. Koblents, Eugenia; Mariño, Inés P.; Míguez, Joaquín: Bayesian computation methods for inference in stochastic kinetic models (2019)
  7. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  8. Maire, Florian; Friel, Nial; Alquier, Pierre: Informed sub-sampling MCMC: approximate Bayesian inference for large datasets (2019)
  9. 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
  10. Ho, Lam Si Tung; Crawford, Forrest W.; Suchard, Marc A.: Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease (2018)
  11. 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)
  12. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  13. 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)
  14. 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)
  15. Nott, David J.; Drovandi, Christopher C.; Mengersen, Kerrie; Evans, Michael: Approximation of Bayesian predictive (p)-values with regression ABC (2018)
  16. Skvortsov, Alex; Ristic, Branko; Kamenev, Alex: Predicting population extinction from early observations of the Lotka-Volterra system (2018)
  17. Spantini, Alessio; Bigoni, Daniele; Marzouk, Youssef: Inference via low-dimensional couplings (2018)
  18. Dennis Prangle: gk: An R Package for the g-and-k and generalised g-and-h Distributions (2017) arXiv
  19. Geppert, Leo N.; Ickstadt, Katja; Munteanu, Alexander; Quedenfeld, Jens; Sohler, Christian: Random projections for Bayesian regression (2017)
  20. Guha, Nilabja; Tan, Xiaosi: Multilevel approximate Bayesian approaches for flows in highly heterogeneous porous media and their applications (2017)

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