The generalist design of the abc package leaves data simulation and summary statistic calculation to users rather than implementing problem-specific simulators. This approach provides flexibility to accommodate different complex models and allows the software to adapt to ongoing developments in ABC methodology. [@csillery_abc_2012]
Definitions
Synthesis
User-defined data simulation is established as the foundational mechanism that allows generalist ABC packages to accommodate diverse statistical models without requiring explicit likelihood calculations, making approximate Bayesian computation accessible for complex models where traditional inference is intractable. The mechanistic relationship operates through a deliberate architectural choice: by allowing users to specify their own data simulation functions, ABC frameworks can remain agnostic to model-specific details while still enabling parameter estimation and model selection through comparison of summary statistics between observed and simulated data. This flexibility is essential because ABC analysis typically follows a trial-and-error approach requiring experimentation with different models and algorithms, which generalist packages must support within a single unified framework. While the core principle of simulation-based inference is well-established, the related concepts leave unresolved questions about how different choices of summary statistics and simulation designs affect the tradeoff between computational tractability and inferential precision across different classes of models.
Related
- ABC enables model selection through comparison of observed and simulated data
- Generalist ABC package accommodates flexible model and algorithm choice
- ABC bypasses likelihood evaluation through data simulation