A method for parameter inference and model selection that bypasses exact likelihood calculations by comparing summary statistics of observed data with those of simulated data generated from a model.
Related Claims
- ABC bypasses likelihood evaluation through data simulation
- ABC enables model selection through comparison of observed and simulated data
- Nonlinear heteroscedastic regression improves ABC parameter estimation accuracy
- Generalist ABC package accommodates flexible model and algorithm choice
- User-defined data simulation enables generalist ABC implementation