Bayesian Divergence Time Calibration Data with Dirichlet Process Prior

Data from a study by Tracy A. Heath presents a hierarchical Bayesian model for calibrating species divergence time estimates using multiple fossil age constraints. The model employs a Dirichlet process prior as a hyperprior on calibration density parameters and was evaluated using both simulated and biological data. The dataset supports the analysis of node age estimates under this hierarchical approach.

Use Cases

• Analyze the clustering of rate-parameters into distinct categories using the Dirichlet process prior hyperprior.
• Compare the accuracy of internal node age estimates from the hierarchical model against fixed exponential prior densities.
• Evaluate the performance of the Dirichlet process hyperprior on simulated data for divergence time calibration.
• Investigate the mixture of exponential distributions sampled for calibrated node ages using Markov chain Monte Carlo methods.

Strengths

• Model validated with both simulated and biological data, demonstrating improved accuracy and precision.
• Dataset is associated with a peer-reviewed methodological study on Bayesian divergence time estimation.
• Released under the permissive CC0 1.0 public domain license for unrestricted use.

Limitations

• The specific data format, size, row count, and column structure are unknown, limiting immediate usability.
• Data is from a 2020 study and may not reflect the most recent methodological developments in the field.
• Without sample data or file formats, significant preprocessing or format discovery may be required.

Provenance

Source

Dryad digital repository.

Collection Method

Data generated from the analysis of a hierarchical Bayesian model for fossil calibration in divergence time estimation.

Freshness

Last updated on 2020-06-24.