diff --git a/examples/conjugate_models.rs b/examples/conjugate_models.rs
new file mode 100644
index 0000000..cc8ef35
--- /dev/null
+++ b/examples/conjugate_models.rs
@@ -0,0 +1,71 @@
+//! Conjugate Bayesian model examples.
+//!
+//! These examples use closed-form posterior updates, then build the matching
+//! bayes-rs distributions for posterior summaries and simple plug-in predictive
+//! checks. The plug-in checks are intentionally lightweight examples; they are
+//! not substitutes for the full posterior-predictive distributions that these
+//! conjugate pairs also provide in closed form. They are deterministic and quick
+//! enough to use as executable docs.
+
+use bayes_rs::distributions::{Beta, Binomial, DiscreteDistribution, Distribution, Gamma, Poisson};
+
+fn beta_binomial_posterior(
+    prior_alpha: f64,
+    prior_beta: f64,
+    successes: u64,
+    trials: u64,
+) -> bayes_rs::Result<Beta> {
+    let failures = trials
+        .checked_sub(successes)
+        .ok_or_else(|| bayes_rs::BayesError::invalid_parameter("successes cannot exceed trials"))?;
+
+    Beta::new(prior_alpha + successes as f64, prior_beta + failures as f64)
+}
+
+fn gamma_poisson_posterior(
+    prior_shape: f64,
+    prior_rate: f64,
+    counts: &[u64],
+) -> bayes_rs::Result<Gamma> {
+    let observed_events: u64 = counts.iter().sum();
+    // bayes-rs parameterizes Gamma as shape/rate, so exposure increments rate.
+    Gamma::new(
+        prior_shape + observed_events as f64,
+        prior_rate + counts.len() as f64,
+    )
+}
+
+fn main() -> bayes_rs::Result<()> {
+    let conversion_posterior = beta_binomial_posterior(2.0, 2.0, 42, 120)?;
+    let posterior_mean = conversion_posterior.mean();
+    let predictive_successes = Binomial::new(25, posterior_mean)?;
+
+    println!("Beta-binomial conversion-rate update");
+    println!("  Posterior mean success probability: {posterior_mean:.3}");
+    println!(
+        "  Plug-in predictive probability of at least 8 successes in 25 trials: {:.3}",
+        (8..=25)
+            .map(|successes| predictive_successes.pmf(successes))
+            .sum::<f64>()
+    );
+
+    let daily_defects = [3, 4, 2, 5, 4, 1, 3];
+    let defect_rate_posterior = gamma_poisson_posterior(1.5, 1.0, &daily_defects)?;
+    let posterior_rate = defect_rate_posterior.mean();
+    let next_day_defects = Poisson::new(posterior_rate)?;
+
+    println!("Gamma-Poisson count-rate update");
+    println!("  Posterior mean event rate: {posterior_rate:.3}");
+    println!(
+        "  Plug-in predictive probability of at most 2 events tomorrow: {:.3}",
+        (0..=2)
+            .map(|count| next_day_defects.pmf(count))
+            .sum::<f64>()
+    );
+    println!(
+        "  Posterior density at rate 3.0: {:.3}",
+        defect_rate_posterior.pdf(3.0)
+    );
+
+    Ok(())
+}
diff --git a/tests/conjugate_models_test.rs b/tests/conjugate_models_test.rs
new file mode 100644
index 0000000..7890758
--- /dev/null
+++ b/tests/conjugate_models_test.rs
@@ -0,0 +1,50 @@
+use approx::assert_relative_eq;
+use bayes_rs::distributions::{Beta, Binomial, DiscreteDistribution, Gamma, Poisson};
+
+#[test]
+fn beta_binomial_update_matches_closed_form_posterior() {
+    let prior_alpha = 2.0;
+    let prior_beta = 2.0;
+    let successes = 42;
+    let trials = 120;
+    let failures = trials - successes;
+
+    let posterior = Beta::new(prior_alpha + successes as f64, prior_beta + failures as f64)
+        .expect("posterior parameters should be valid");
+
+    assert_relative_eq!(posterior.alpha(), 44.0);
+    assert_relative_eq!(posterior.beta(), 80.0);
+    assert_relative_eq!(posterior.mean(), 44.0 / 124.0);
+
+    let plug_in_predictive =
+        Binomial::new(25, posterior.mean()).expect("posterior mean is a probability");
+    let probability_at_least_eight: f64 = (8..=25).map(|k| plug_in_predictive.pmf(k)).sum();
+
+    assert!(probability_at_least_eight > 0.71);
+    assert!(probability_at_least_eight < 0.72);
+}
+
+#[test]
+fn gamma_poisson_update_matches_closed_form_posterior() {
+    let prior_shape = 1.5;
+    let prior_rate = 1.0;
+    let counts = [3_u64, 4, 2, 5, 4, 1, 3];
+    let observed_events: u64 = counts.iter().sum();
+
+    let posterior = Gamma::new(
+        prior_shape + observed_events as f64,
+        prior_rate + counts.len() as f64,
+    )
+    .expect("posterior parameters should be valid");
+
+    assert_relative_eq!(posterior.shape(), 23.5);
+    assert_relative_eq!(posterior.rate(), 8.0);
+    assert_relative_eq!(posterior.mean(), 23.5 / 8.0);
+
+    let plug_in_predictive =
+        Poisson::new(posterior.mean()).expect("posterior mean is a valid rate");
+    let probability_at_most_two: f64 = (0..=2).map(|k| plug_in_predictive.pmf(k)).sum();
+
+    assert!(probability_at_most_two > 0.42);
+    assert!(probability_at_most_two < 0.44);
+}