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.

References in zbMATH (referenced in 51 articles )

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  1. Cabras, Stefano; Castellanos, María Eugenia; Ratmann, Oliver: Goodness of fit for models with intractable likelihood (2021)
  2. Dmitrieva, Tatiana; McCullough, Kristin; Ebrahimi, Nader: Improved approximate Bayesian computation methods via empirical likelihood (2021)
  3. Klein, Nadja; Nott, David J.; Smith, Michael Stanley: Marginally calibrated deep distributional regression (2021)
  4. Pacchiardi, Lorenzo; Künzli, Pierre; Chopard, Bastien; Schöngens, Marcel; Dutta, Ritabrata: Distance-learning for approximate Bayesian computation to model a volcanic eruption (2021)
  5. Simola, Umberto; Cisewski-Kehe, Jessi; Gutmann, Michael U.; Corander, Jukka: Adaptive approximate Bayesian computation tolerance selection (2021)
  6. Simola, Umberto; Cisewski-Kehe, Jessi; Wolpert, Robert L.: Approximate Bayesian computation for finite mixture models (2021)
  7. Arcede, Jayrold P.; Caga-Anan, Randy L.; Mentuda, Cheryl Q.; Mammeri, Youcef: Accounting for symptomatic and asymptomatic in a SEIR-type model of COVID-19 (2020)
  8. Billig Rose, Erica; Roy, Jason A.; Castillo-Neyra, Ricardo; Ross, Michelle E.; Condori-Pino, Carlos; Peterson, Jennifer K.; Naquira-Velarde, Cesar; Levy, Michael Z.: A real-time search strategy for finding urban disease vector infestations (2020)
  9. Economou, P.; Tzavelas, G.; Batsidis, A.: Robust inference under r-size-biased sampling without replacement from finite population (2020)
  10. Jhwueng, Dwueng-Chwuan: Modeling rate of adaptive trait evolution using Cox-Ingersoll-Ross process: an approximate Bayesian computation approach (2020)
  11. Mammeri, Youcef: A reaction-diffusion system to better comprehend the unlockdown: application of SEIR-type model with diffusion to the spatial spread of COVID-19 in France (2020)
  12. Thong, David; Streftaris, George; Gibson, Gavin J.: Latent likelihood ratio tests for assessing spatial kernels in epidemic models (2020)
  13. van den Bos, Laurent; Sanderse, Benjamin; Bierbooms, Wim; van Bussel, Gerard: Bayesian model calibration with interpolating polynomials based on adaptively weighted Leja nodes (2020)
  14. Griswold, Cortland K.: An ancestral process with selection in an ecological community (2019)
  15. Izbicki, Rafael; Lee, Ann B.; Pospisil, Taylor: ABC-CDE: toward approximate Bayesian computation with complex high-dimensional data and limited simulations (2019)
  16. Ke, Yuqin; Tian, Tianhai: Approximate Bayesian computational methods for the inference of unknown parameters (2019)
  17. Kobayashi, Genya; Kakamu, Kazuhiko: Approximate Bayesian computation for Lorenz curves from grouped data (2019)
  18. Koblents, Eugenia; Mariño, Inés P.; Míguez, Joaquín: Bayesian computation methods for inference in stochastic kinetic models (2019)
  19. Lee, Jeong Eun; Nicholls, Geoff K.; Ryder, Robin J.: Calibration procedures for approximate Bayesian credible sets (2019)
  20. Maire, Florian; Friel, Nial; Alquier, Pierre: Informed sub-sampling MCMC: approximate Bayesian inference for large datasets (2019)

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