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

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 51 articles )

Showing results 1 to 20 of 51.
Sorted by year (citations)

1 2 3 next

  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)

1 2 3 next