ABC performs model selection by using summary statistics derived from observed and simulated data rather than evaluating likelihood functions. This approach allows users to fit different models to their data and compare models within a Bayesian framework, which is integral to any Bayesian analysis. [@csillery_abc_2012]
Definitions
Synthesis
Approximate Bayesian computation enables model selection by generating simulated data from candidate models and comparing their summary statistics to those of observed data, thereby circumventing the need for analytically tractable likelihood functions. This simulation-based comparison allows researchers to evaluate which models best reproduce key features of real data through summary statistics that capture relevant information without requiring full likelihood calculations. The approach operates within a Bayesian framework where models are assessed based on how well their simulated outputs match observed patterns, making it particularly valuable for complex stochastic models where traditional likelihood-based model selection would be computationally prohibitive or impossible. However, the effectiveness of this model selection strategy depends critically on choosing appropriate summary statistics and algorithms, an area where generalist implementations provide flexibility but require users to navigate trial-and-error processes to optimize inferential precision against computational tractability.
Related
- User-defined data simulation enables generalist ABC implementation
- Generalist ABC package accommodates flexible model and algorithm choice
- ABC bypasses likelihood evaluation through data simulation