fix(scoring): distinguish approximate evidence from exact marginal likelihood

README.md

@@ -9,6 +9,6 @@ We start from the most specific grammar the data permits: every sample contribut
 
 From there, we generalise with two operators, merging and chunking. Merging takes a pair of nonterminals and folds them into a single nonterminal containing the union of their productions; chunking replaces a contiguous sequence of symbols with a fresh nonterminal. Chunking doesn't itself change the language the grammar generates, but it changes the internal structure in a way that can expose useful merges which weren't previously available.
 
-We rank candidate grammars by the posterior `P(M | X) ∝ P(M) P(X | M)`, where the prior is a description length and the likelihood integrates over the production probabilities under symmetric Dirichlet priors. The scoring therefore accounts for uncertainty in the production probabilities, rather than relying on a single fitted parameterisation.
+We rank candidate grammars with a structural description length and an approximate evidence objective. The default scorer fits production probabilities with expectation maximisation, computes fractional expected production counts, and applies a symmetric Dirichlet correction. Maximum likelihood and a variational evidence lower bound are also available.
 
 We explore the resulting grammar space using either beam or best first search, then fit the parameters by expectation maximisation once the grammar structure is fixed. Parsing is a generalised CYK or inside computation over spans, which lets the grammar be scored directly without an intermediate conversion into Chomsky normal form.