4. PyMsBayes Tutorials

• 4.1. Background
  • 4.1.1. Comparative divergence models
  • 4.1.2. Bayesian divergence-model choice
  • 4.1.3. Approximate-likelihood Bayesian computation
  • 4.1.4. Prior on divergence times
  • 4.1.5. Prior on divergence models
  • 4.1.6. Re-sorting the taxa during the ABC algorithm
• 4.2. The Configuration File
  • 4.2.1. The Preamble
  • 4.2.2. The Sample Table
• 4.3. Selecting Priors
  • 4.3.1. An introduction to the gamma probability distribution
  • 4.3.2. Important priors for the dpp-msbayes model
• 4.4. Getting the exampe data
• 4.5. A Simple Empirical Analysis
  • 4.5.1. The data files
  • 4.5.2. The primary analysis program dmc.py
  • 4.5.3. Plotting the results
  • 4.5.4. Summarzing results about divergence-time scenarios
• 4.6. An Empirical Analysis under Multiple Models
  • 4.6.1. The output
  • 4.6.2. Output from a longer analysis
  • 4.6.3. Summarzing results about divergence-time scenarios
• 4.7. A Simulation-based Validation Analysis
  • 4.7.1. Background
  • 4.7.2. The example configs for simulated datasets
  • 4.7.3. Running the simulation-based analysis
  • 4.7.4. The output
  • 4.7.5. Summarizing the results
  • 4.7.6. CV accuracy plots
  • 4.7.7. Histograms of the estimated number of divergence events
  • 4.7.8. Histograms of the support for the single divergence model
• 4.8. A key to the output of model parameters and associated statstics
  • 4.8.1. The parameters
  • 4.8.2. The statistics