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Phylogenetic Likelihood Inference L1-409

Computational BiologyMolecular evolutionδ=5 · challengingL_DAG = 4.5📋 Stub — not mineable
📋

Unclaimed Principle — open for contribution

This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.

To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.

Forward model E

Phylogenetic Likelihood Inference: Phylogenetic maximum likelihood: reconstruct evolutionary tree topology and branch lengths from DNA/protein alignment. The forward operator produces the measurement through a 3-node primitive DAG (M.felsenstein.pruning_algorithm…); recovery is posed as a nonlinear_inverse problem. Difficulty tier delta=5 with effective condition number kappa_eff~2000; model_misspecification_substitution, long_branch_attraction set the accuracy floor at the Omega boundary. See the forward_model field for the closed-form equation.

L-DAG

N.pointwise -> S.mcmc.tree_space_bayesian -> O.atr.total_tree_likelihood
N.pointwiseS.mcmc.tree_space_bayesianO.atr.total_tree_likelihood

Well-posedness W

Existence:
true
Uniqueness:
true
Stability:
conditional
κ:
100000

Existence of the recovered phylogenetic_tree_topology is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 2000); model_misspecification_substitution dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Multinomial sets the irreducible data-fidelity floor.

Solvability C

Solver class:
statistical [IQ-TREE2_ML or MrBayes_MCMC or BEAST2]
Convergence rate q:
2
Complexity:
O(N_taxa ** 2 * N_sites) per tree evaluation with Felsenstein pruning per iteration

Specs (0)

No L2 specs registered yet for this principle.