Results 11 to 20 of about 137,667 (314)

Approximate Bayesian computation.

PLoS Computational Biology, 2013
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the ...
Mikael Sunnåker, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, Christophe Dessimoz   +5 more
doaj   +9 more sources

Approximate Bayesian Computation for Copula Estimation

Statistica, 2015
We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution and it is based on the properties of an Approximate Bayesian Monte\,Carlo ...
Clara Grazian, Brunero Liseo
doaj   +3 more sources

Adaptive approximate Bayesian computation for complex models [PDF]

Computational Statistics, 2013
Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These techniques allow to fi t a model to data without relying on the computation of the model likelihood.
CC Drovandi, D Wegmann, D Wegmann, Franck Jabot, Guillaume Deffuant, MA Beaumont, Maxime Lenormand, MGB Blum, P Glynn, P Marjoram, P Moral Del, P Moral Del, SA Sisson, T Toni   +13 more
core   +11 more sources

Approximate Bayesian Computation of radiocarbon and paleoenvironmental record shows population resilience on Rapa Nui (Easter Island) [PDF]

Nature Communications, 2021
Summed probability distributions of radiocarbon dates can be used to estimate past demography, but methods to test for associations with environmental change are lacking. Here, DiNapoli et al.
Robert J. DiNapoli, Enrico R. Crema, Carl P. Lipo, Timothy M. Rieth, Terry L. Hunt   +4 more
doaj   +2 more sources

Approximate Bayesian Computation with composite score functions [PDF]

Statistics and Computing, 2015
Both Approximate Bayesian Computation (ABC) and composite likelihood methods are useful for Bayesian and frequentist inference, respectively, when the likelihood function is intractable.
Ruli, Erlis, Sartori, Nicola, Ventura, Laura   +2 more
core   +2 more sources

An automatic adaptive method to combine summary statistics in approximate Bayesian computation. [PDF]

PLoS ONE, 2020
To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter
Jonathan U Harrison, Ruth E Baker
doaj   +2 more sources

Modelling plant disease spread and containment: Simulation and approximate Bayesian Computation for Xylella fastidiosa in Puglia, Italy. [PDF]

PLoS Comput Biol
Chapman D, Occhibove F, Bullock JM, Beck PSA, Navas-Cortes JA, White SM.   +5 more
europepmc   +2 more sources

Multifidelity Approximate Bayesian Computation [PDF]

SIAM/ASA Journal on Uncertainty Quantification, 2020
25 pages plus Supplementary Material (as appendices)
Prescott, T, Baker, R
openaire   +4 more sources

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting [PDF]

, 2022
Valeriano JP, Cintra PH, Libotte G, Reis I, Fontinele F, Silva R, Malta S.   +6 more
europepmc   +2 more sources

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism.

Neuroimage, 2021
West TO, Berthouze L, Farmer SF, Cagnan H, Litvak V.   +4 more
europepmc   +2 more sources