Results 81 to 90 of about 135,519 (174)

Approximate Bayesian Computation for Estimating Parameters of Data-Consistent Forbush Decrease Model

Entropy, 2018
Realistic modeling of complex physical phenomena is always quite a challenging task. The main problem usually concerns the uncertainties surrounding model input parameters, especially when not all information about a modeled phenomenon is known.
Anna Wawrzynczak, Piotr Kopka
doaj   +1 more source

Probabilistic Updating of Structural Models for Damage Assessment Using Approximate Bayesian Computation

Sensors, 2020
A novel probabilistic approach for model updating based on approximate Bayesian computation with subset simulation (ABC-SubSim) is proposed for damage assessment of structures using modal data.
Zhouquan Feng, Yang Lin, Wenzan Wang, Xugang Hua, Zhengqing Chen   +4 more
doaj   +1 more source

Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations

IEEE Access
This study addresses the challenge of estimating parameters for two Gamma populations that share a common scale parameter but differ in their shape parameters, within the context of fuzzy data.
Vijay Kumar Lingutla, Nagamani Nadiminti
doaj   +1 more source

AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning

Entropy, 2019
Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of ABC-
Wilson González-Vanegas, Andrés Álvarez-Meza, José Hernández-Muriel, Álvaro Orozco-Gutiérrez   +3 more
doaj   +1 more source

Approximate Bayesian Computation in State Space Models

, 2014
A new approach to inference in state space models is proposed, based on approximate Bayesian computation (ABC). ABC avoids evaluation of the likelihood function by matching observed summary statistics with statistics computed from data simulated from the
Maneesoonthorn, Worapree, Martin, Gael M., McCabe, Brendan P. M., Robert, Christian P.   +3 more
core  

Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania

Nature Communications, 2019
Introgression of Neanderthals and Denisovans left genomic signals in anatomically modern human after Out-of-Africa event. Here, the authors identify a third archaic introgression common to all Asian and Oceanian human populations by applying an ...
Mayukh Mondal, Jaume Bertranpetit, Oscar Lao   +2 more
doaj   +1 more source

ABCtoolbox: a versatile toolkit for approximate Bayesian computations

BMC Bioinformatics, 2010
Background The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference
Neuenschwander Samuel, Leuenberger Christoph, Wegmann Daniel, Excoffier Laurent   +3 more
doaj   +1 more source

Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python

Epidemics, 2018
Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to ...
Michael A. Irvine, T. Déirdre Hollingsworth   +1 more
doaj   +1 more source

Robustifying Approximate Bayesian Computation

Approximate Bayesian computation (ABC) is one of the most popular "likelihood-free" methods. These methods have been applied in a wide range of fields by providing solutions to intractable likelihood problems in which exact Bayesian approaches are either infeasible or computationally costly.
Weerasinghe, Chaya, Frazier, David T., Loaiza-Maya, Ruben, Drovandi, Christopher   +3 more
openaire   +2 more sources

Multivariate adaptive regression splines-assisted approximate Bayesian computation for calibration of complex hydrological models

Journal of Hydroinformatics
Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological ...
Jinfeng Ma, Ruonan Li, Hua Zheng, Weifeng Li, Kaifeng Rao, Yanzheng Yang, Bo Wu   +6 more
doaj   +1 more source