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// playbooks / bayesian-methods · kb_findings_as_priors

KB Findings as Informative Priors

step_count: 7· runtime: —

Formalize the "killer demo" of Bayesian + PAX — extract quantitative effect sizes from a knowledge-base pack's findings.json, convert them into informative priors on corresponding regression coefficients, and fit Bayesian linear regression on new data. Produces a posterior that formally blends prior literature with current evidence, with posterior predictive checks for model adequacy.

// pipeline
7 steps· DAG
01

Load Findings from Source Pack

action data_quality_gateon fail: abort
config (3 keys)
{
  "confidence_min": "moderate",
  "min_findings_with_effects": 3,
  "required_fields": [
    "construct_ids",
    "effect_size_value",
    "effect_size_se"
  ]
}
02

Map Findings to Regression Coefficients

action analyzeload_kb_findings
config (4 keys)
{
  "coefficient_prior_mean": "effect_size_value",
  "coefficient_prior_sd": "effect_size_se",
  "match_on": "construct_ids",
  "scale_inflation": 1.5
}
03

Specify Weakly Informative Priors for Unmapped Parameters

config (4 keys)
{
  "intercept_prior": "normal(0, 10)",
  "reference": "gelman_2006_variance_priors",
  "sigma_prior": "half_cauchy(0, 2.5)",
  "unmapped_coef_prior": "normal(0, 2.5)"
}
04

Fit Bayesian Linear Regression via NUTS

engine bayesian_linear_regressionspecify_weakly_informative_defaults
config (5 keys)
{
  "adapt_delta": 0.95,
  "chains": 4,
  "iter_sampling": 2000,
  "iter_warmup": 1000,
  "sampler": "no_u_turn_sampler"
}
expected results (2 keys)
{
  "max_rhat": 1.01,
  "min_effective_sample_size": 400
}
05

Posterior Predictive Check

action analyzefit_bayesian_regression
config (3 keys)
{
  "flag_bayesian_p_extreme": 0.05,
  "n_replications": 1000,
  "test_quantities": [
    "mean",
    "sd",
    "q05",
    "q95"
  ]
}
06

Prior Sensitivity Analysis

action analyzefit_bayesian_regression
config (2 keys)
{
  "report": "posterior_summary_comparison",
  "scale_multipliers": [
    0.5,
    1,
    2,
    5
  ]
}
07

Posterior Summary with Credible Intervals

config (2 keys)
{
  "include_prior_comparison": true,
  "statistics": [
    "posterior_mean",
    "posterior_median",
    "credible_interval_95",
    "credible_interval_50",
    "probability_direction"
  ]
}
// from pax
Bayesian Methods
// engines
engine.bayesian_linear_regression
// note
step bodies extracted from the .pax archive at build time. download the parent pax for the full yaml.
[ download bayesian-methods.pax.tar.gz ]