Mixed Path HMC Sampling Methods for Molecular Tree Spaces
Bibliographic record
Abstract
With the increasing abundance of modern molecular sequence data and the dramatic expansion of the tree-like topological space describing historical relationships between species,reliable inference of phylogenetic trees continues to face enormous challenges.In recent years,the most advanced Hamiltonian Markov Monte Carlo(HMC) algorithm in the Markov Chain Monte Carlo(MCMC) family has been shown to be applicable to phylogenetic analysis,which can avoid the large amount of random walk behaviors present in traditional MCMC algorithms and speed up the mixing of Markov chains.However,in the more complex multimodal development tree space,the HMC algorithm cannot escape from the local high probability region by obtaining propo-sals from other modes.In order to improve the robustness of the algorithm,a hybrid path Hamiltonian Markov Monte Carlo(MPHMC) optimization strategy is proposed in this paper.Without adding additional computational cost,the algorithm samples paths with a non-HMC update component for discrete parameters,alternating with HMC deterministic updates,and introduces a branch rearrangement strategy with greater topological variation in the tree space,enabling freer traversal of the entire posterior distribution's tree space.Experiments on five empirical datasets demonstrate that the MPHMC method better samples from the correct posterior distribution,and the HMC single-path sampling algorithm may fail when run on larger datasets that are more difficult to sample,while the MPHMC method achieves a sampling efficiency gain over 14% than the widely used phylogenetic analysis tool,Mrbayes(MCMC).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".