bio-phylo-bayesian-inference
Run Bayesian phylogenetic analysis with MrBayes, BEAST2, RevBayes, and PhyloBayes including MCMC convergence diagnostics and model comparison. Use when needing posterior probability support, Bayesian model averaging, site-heterogeneous models for deep phylogenies, or formal model comparison via stepping-stone sampling.
npx skills add BioTender-max/awesome-bio-agent-skills --skill bayesian-inference --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: MrBayes 3.2.7+, BEAST2 2.7+, Tracer 1.7+, RevBayes 1.2+, PhyloBayes MPI 1.9+, RWTY (R package) Before using code patterns, verify installed versions match. If versions differ: - CLI: `mb --version`, `beast -version`, `rb --version`, `pb --version` - Python: `pip show biopython` then `help(module.function)` to check signatures - R: `packageVersion('rwty')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Bayesian Phylogenetic Inference **"Run a Bayesian phylogenetic analysis"** -> Infer posterior distribution of trees and parameters via MCMC sampling, producing posterior probability support and enabling formal model comparison. - CLI: `mb` (MrBayes), `beast` (BEAST2), `rb` (RevBayes), `pb`/`bpcomp` (PhyloBayes) - Python: BioPython `Bio.Phylo` for parsing output trees; `arviz`/`pandas` for trace diagnostics ## When to Use Bayesian vs ML | Factor | ML (IQ-TREE/RAxML-NG) | MrBayes | BEAST2 | RevBayes | PhyloBayes | |--------|------------------------|---------|--------|---------
- Version Compatibility
- When to Use Bayesian vs ML
- MrBayes
- Basic Analysis
- MC3 (Metropolis-Coupled MCMC)
- Mixed Models and Partitions
- Reversible-Jump Model Selection
- BEAST2
- Workflow
- bModelTest for Bayesian Model Averaging
- Running Multiple Independent Analyses
- MCMC Convergence Diagnostics
- Effective Sample Size (ESS)
- Trace Plot Interpretation
beast -threads 4 -seed 12345 analysis.xml treeannotator -burnin 10 -heights median analysis.trees consensus.tree beast -threads 4 -seed 67890 analysis.xml beast -threads 4 model_selection.xml Run two independent chains (mandatory for convergence assessment) mpirun -np 8 pb_mpi -d alignment.phy -cat -gtr -x 10 5000 chain1 & mpirun -np 8 pb_mpi -d alignment.phy -cat -gtr -x 10 5000 chain2 & After chains complete, check convergence bpcomp -x 1000 chain1 chain2 tracecomp -x 1000 chain1 chain2
What does the bio-phylo-bayesian-inference skill do?
Run Bayesian phylogenetic analysis with MrBayes, BEAST2, RevBayes, and PhyloBayes including MCMC convergence diagnostics and model comparison. Use when needing posterior probability support, Bayesian model averaging, site-heterogeneous models for deep phylogenies, or formal model comparison via stepping-stone sampling.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill bayesian-inference --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.