Bayesian program synthesis

Bayesian program synthesis is a method in the fields of machine learning and artificial intelligence focused on automatically generating programs or code from high-level specifications or examples. This approach employs Bayesian inference, which allows for the incorporation of uncertainty and prior beliefs into the learning process. Key components of Bayesian program synthesis include: 1. **Probabilistic Models**: Bayesian program synthesis uses probabilistic models to represent both the space of possible programs and the uncertainty about which program is the best match for the given specifications or examples.