A minimal probabilistic Prolog meta-interpreter
A technical exploration of a minimal probabilistic Prolog meta-interpreter for stochastic simulation.
A technical exploration of a minimal probabilistic Prolog meta-interpreter for stochastic simulation.
Explores using logic programming (Prolog) for data analysis, demonstrating its application on a diamond pricing dataset to build robust models.
Explores using logic programming and Prolog for semi-supervised clustering, arguing it's more intuitive than traditional algorithms for rule-based problems.