Bayesian Posterior Inference Results for Simulated Bacterial Transmission Scenarios

Sanni Översti's summary of Bayesian posterior inference results from 100 simulation replicates for hospital and community-driven transmission scenarios. The dataset, last updated on March 17, 2026, includes metrics like effective sample size, relative error, bias, and 95% highest posterior density interval accuracy for epidemiological parameters. It is a 9.5 KB Excel file shared under a CC-BY-4.0 license on figshare.

Use Cases

• Validate Bayesian inference performance based on effective sample size (ESS) metrics across 100 simulation replicates.
• Compare parameter estimation accuracy between hospital-driven, equal, and community-driven transmission scenarios.
• Assess the reliability of 95% highest posterior density (HPD) intervals based on the reported accuracy metric.

Strengths

• Results are based on 100 simulation replicates per scenario, providing a basis for statistical evaluation.
• Includes specific quality metrics like effective sample size (ESS) with a threshold of 200.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description references a community sampling rate but the value is not provided.

Provenance

Source

figshare, author Sanni Översti

Collection Method

Results from simulation scenarios, likely generated for a research study.

Freshness

Last updated 2026-03-17 16:41:43