diff --git a/README.md b/README.md
index 7949823..cde16e6 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 study
     .optimize_with_sampler(20, |trial| {
         let x = trial.suggest_float("x", -10.0, 10.0)?;
-        Ok::<_, optimizer::TpeError>(x * x)
+        Ok::<_, optimizer::Error>(x * x)
     })
     .unwrap();
 
diff --git a/src/error.rs b/src/error.rs
index d6cff2f..fe34b47 100644
--- a/src/error.rs
+++ b/src/error.rs
@@ -1,10 +1,5 @@
-//! Error types for the optimizer library.
-
-use thiserror::Error;
-
-/// The error type for TPE operations.
-#[derive(Debug, Error)]
-pub enum TpeError {
+#[derive(Debug, thiserror::Error)]
+pub enum Error {
     /// Returned when the lower bound is greater than the upper bound.
     #[error("invalid bounds: low ({low}) must be less than or equal to high ({high})")]
     InvalidBounds {
@@ -61,5 +56,4 @@ pub enum TpeError {
     TaskError(String),
 }
 
-/// A specialized Result type for TPE operations.
-pub type Result<T> = core::result::Result<T, TpeError>;
+pub type Result<T> = core::result::Result<T, Error>;
diff --git a/src/kde.rs b/src/kde.rs
index 686d9e6..0235955 100644
--- a/src/kde.rs
+++ b/src/kde.rs
@@ -5,7 +5,7 @@
 
 use rand::Rng;
 
-use crate::error::{Result, TpeError};
+use crate::error::{Error, Result};
 
 /// A Gaussian kernel density estimator for continuous distributions.
 ///
@@ -45,10 +45,10 @@ impl KernelDensityEstimator {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::EmptySamples` if `samples` is empty.
+    /// Returns `Error::EmptySamples` if `samples` is empty.
     pub(crate) fn new(samples: Vec<f64>) -> Result<Self> {
         if samples.is_empty() {
-            return Err(TpeError::EmptySamples);
+            return Err(Error::EmptySamples);
         }
 
         let bandwidth = Self::scotts_rule(&samples);
@@ -61,14 +61,14 @@ impl KernelDensityEstimator {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::EmptySamples` if `samples` is empty.
-    /// Returns `TpeError::InvalidBandwidth` if `bandwidth` is not positive.
+    /// Returns `Error::EmptySamples` if `samples` is empty.
+    /// Returns `Error::InvalidBandwidth` if `bandwidth` is not positive.
     pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Result<Self> {
         if samples.is_empty() {
-            return Err(TpeError::EmptySamples);
+            return Err(Error::EmptySamples);
         }
         if bandwidth <= 0.0 {
-            return Err(TpeError::InvalidBandwidth(bandwidth));
+            return Err(Error::InvalidBandwidth(bandwidth));
         }
 
         Ok(Self { samples, bandwidth })
@@ -261,20 +261,20 @@ mod tests {
     fn test_kde_empty_samples() {
         let samples: Vec<f64> = vec![];
         let result = KernelDensityEstimator::new(samples);
-        assert!(matches!(result, Err(TpeError::EmptySamples)));
+        assert!(matches!(result, Err(Error::EmptySamples)));
     }
 
     #[test]
     fn test_kde_zero_bandwidth() {
         let samples = vec![1.0, 2.0, 3.0];
         let result = KernelDensityEstimator::with_bandwidth(samples, 0.0);
-        assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
+        assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
     }
 
     #[test]
     fn test_kde_negative_bandwidth() {
         let samples = vec![1.0, 2.0, 3.0];
         let result = KernelDensityEstimator::with_bandwidth(samples, -1.0);
-        assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
+        assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
     }
 }
diff --git a/src/lib.rs b/src/lib.rs
index 8b4c1eb..a0660df 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -33,7 +33,7 @@
 //! study
 //!     .optimize_with_sampler(20, |trial| {
 //!         let x = trial.suggest_float("x", -10.0, 10.0)?;
-//!         Ok::<_, optimizer::TpeError>(x * x)
+//!         Ok::<_, optimizer::Error>(x * x)
 //!     })
 //!     .unwrap();
 //!
@@ -86,7 +86,7 @@
 //!         let optimizer = trial.suggest_categorical("optimizer", &["sgd", "adam", "rmsprop"])?;
 //!
 //!         // Return objective value
-//!         Ok::<_, optimizer::TpeError>(x * n as f64)
+//!         Ok::<_, optimizer::Error>(x * n as f64)
 //!     })
 //!     .unwrap();
 //! ```
@@ -140,7 +140,7 @@ mod study;
 mod trial;
 mod types;
 
-pub use error::{Result, TpeError};
+pub use error::{Error, Result};
 pub use param::ParamValue;
 pub use study::Study;
 pub use trial::Trial;
diff --git a/src/sampler/tpe.rs b/src/sampler/tpe.rs
index 7ef57f1..b4f5a6e 100644
--- a/src/sampler/tpe.rs
+++ b/src/sampler/tpe.rs
@@ -9,7 +9,7 @@ use rand::rngs::StdRng;
 use rand::{Rng, SeedableRng};
 
 use crate::distribution::Distribution;
-use crate::error::{Result, TpeError};
+use crate::error::{Error, Result};
 use crate::kde::KernelDensityEstimator;
 use crate::param::ParamValue;
 use crate::sampler::{CompletedTrial, Sampler};
@@ -106,8 +106,8 @@ impl TpeSampler {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::InvalidGamma` if gamma is not in (0.0, 1.0).
-    /// Returns `TpeError::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
+    /// Returns `Error::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Returns `Error::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
     pub fn with_config(
         gamma: f64,
         n_startup_trials: usize,
@@ -116,12 +116,12 @@ impl TpeSampler {
         seed: Option<u64>,
     ) -> Result<Self> {
         if gamma <= 0.0 || gamma >= 1.0 {
-            return Err(TpeError::InvalidGamma(gamma));
+            return Err(Error::InvalidGamma(gamma));
         }
         if let Some(bw) = kde_bandwidth
             && bw <= 0.0
         {
-            return Err(TpeError::InvalidBandwidth(bw));
+            return Err(Error::InvalidBandwidth(bw));
         }
 
         let rng = match seed {
@@ -484,7 +484,7 @@ impl TpeSamplerBuilder {
     /// # Note
     ///
     /// Validation happens at `build()` time. If gamma is not in (0.0, 1.0),
-    /// `build()` will return `Err(TpeError::InvalidGamma)`.
+    /// `build()` will return `Err(Error::InvalidGamma)`.
     #[must_use]
     pub fn gamma(mut self, gamma: f64) -> Self {
         self.gamma = gamma;
@@ -569,7 +569,7 @@ impl TpeSamplerBuilder {
     /// # Note
     ///
     /// Validation happens at `build()` time. If bandwidth is not positive,
-    /// `build()` will return `Err(TpeError::InvalidBandwidth)`.
+    /// `build()` will return `Err(Error::InvalidBandwidth)`.
     #[must_use]
     pub fn kde_bandwidth(mut self, bandwidth: f64) -> Self {
         self.kde_bandwidth = Some(bandwidth);
@@ -602,8 +602,8 @@ impl TpeSamplerBuilder {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::InvalidGamma` if gamma is not in (0.0, 1.0).
-    /// Returns `TpeError::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
+    /// Returns `Error::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Returns `Error::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
     ///
     /// # Examples
     ///
@@ -817,13 +817,13 @@ mod tests {
     #[test]
     fn test_tpe_sampler_invalid_gamma_zero() {
         let result = TpeSampler::with_config(0.0, 10, 24, None, None);
-        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
+        assert!(matches!(result, Err(Error::InvalidGamma(_))));
     }
 
     #[test]
     fn test_tpe_sampler_invalid_gamma_one() {
         let result = TpeSampler::with_config(1.0, 10, 24, None, None);
-        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
+        assert!(matches!(result, Err(Error::InvalidGamma(_))));
     }
 
     #[test]
@@ -1077,7 +1077,7 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_invalid_gamma() {
         let result = TpeSamplerBuilder::new().gamma(1.5).build();
-        assert!(matches!(result, Err(TpeError::InvalidGamma(_))));
+        assert!(matches!(result, Err(Error::InvalidGamma(_))));
     }
 
     #[test]
diff --git a/src/study.rs b/src/study.rs
index 4526066..e9fa0e0 100644
--- a/src/study.rs
+++ b/src/study.rs
@@ -275,7 +275,7 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials have been completed.
+    /// Returns `Error::NoCompletedTrials` if no trials have been completed.
     ///
     /// # Examples
     ///
@@ -305,7 +305,7 @@ where
         let trials = self.completed_trials.read();
 
         if trials.is_empty() {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         let best = trials
@@ -325,7 +325,7 @@ where
                     }
                 }
             })
-            .ok_or(crate::TpeError::NoCompletedTrials)?;
+            .ok_or(crate::Error::NoCompletedTrials)?;
 
         Ok(best.clone())
     }
@@ -338,7 +338,7 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials have been completed.
+    /// Returns `Error::NoCompletedTrials` if no trials have been completed.
     ///
     /// # Examples
     ///
@@ -387,7 +387,7 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
@@ -402,7 +402,7 @@ where
     /// study
     ///     .optimize(10, |trial| {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///         Ok::<_, optimizer::TpeError>(x * x)
+    ///         Ok::<_, optimizer::Error>(x * x)
     ///     })
     ///     .unwrap();
     ///
@@ -431,7 +431,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -455,7 +455,7 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
@@ -474,7 +474,7 @@ where
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Simulate async work (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -511,7 +511,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -536,8 +536,8 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
-    /// Returns `TpeError::TaskError` if the semaphore is closed or a spawned task panics.
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::TaskError` if the semaphore is closed or a spawned task panics.
     ///
     /// # Examples
     ///
@@ -556,7 +556,7 @@ where
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Async objective function (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -590,7 +590,7 @@ where
                 .clone()
                 .acquire_owned()
                 .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?;
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?;
             let trial = self.create_trial();
             let objective = Arc::clone(&objective);
 
@@ -607,7 +607,7 @@ where
         for handle in handles {
             match handle
                 .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?
             {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
@@ -620,7 +620,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -643,9 +643,9 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully
+    /// Returns `Error::NoCompletedTrials` if no trials completed successfully
     /// before optimization stopped (either by completing all trials or early stopping).
-    /// Returns `TpeError::Internal` if a completed trial is not found after adding (internal invariant violation).
+    /// Returns `Error::Internal` if a completed trial is not found after adding (internal invariant violation).
     ///
     /// # Examples
     ///
@@ -664,7 +664,7 @@ where
     ///         100,
     ///         |trial| {
     ///             let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///             Ok::<_, optimizer::TpeError>(x * x)
+    ///             Ok::<_, optimizer::Error>(x * x)
     ///         },
     ///         |_study, completed_trial| {
     ///             // Stop early if we find a value less than 1.0
@@ -702,7 +702,7 @@ where
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
                     let Some(completed) = trials.last() else {
-                        return Err(crate::TpeError::Internal(
+                        return Err(crate::Error::Internal(
                             "completed trial not found after adding",
                         ));
                     };
@@ -725,7 +725,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -783,7 +783,7 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
@@ -798,7 +798,7 @@ impl Study<f64> {
     /// study
     ///     .optimize_with_sampler(10, |trial| {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///         Ok::<_, optimizer::TpeError>(x * x)
+    ///         Ok::<_, optimizer::Error>(x * x)
     ///     })
     ///     .unwrap();
     ///
@@ -829,7 +829,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -851,8 +851,8 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully.
-    /// Returns `TpeError::Internal` if a completed trial is not found after adding (internal invariant violation).
+    /// Returns `Error::NoCompletedTrials` if no trials completed successfully.
+    /// Returns `Error::Internal` if a completed trial is not found after adding (internal invariant violation).
     ///
     /// # Examples
     ///
@@ -871,7 +871,7 @@ impl Study<f64> {
     ///         100,
     ///         |trial| {
     ///             let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///             Ok::<_, optimizer::TpeError>(x * x)
+    ///             Ok::<_, optimizer::Error>(x * x)
     ///         },
     ///         |study, _completed_trial| {
     ///             // Stop after finding 5 good trials
@@ -907,7 +907,7 @@ impl Study<f64> {
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
                     let Some(completed) = trials.last() else {
-                        return Err(crate::TpeError::Internal(
+                        return Err(crate::Error::Internal(
                             "completed trial not found after adding",
                         ));
                     };
@@ -930,7 +930,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -953,7 +953,7 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
@@ -972,7 +972,7 @@ impl Study<f64> {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Simulate async work (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -1009,7 +1009,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -1034,8 +1034,8 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
-    /// Returns `TpeError::TaskError` if the semaphore is closed or a spawned task panics.
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::TaskError` if the semaphore is closed or a spawned task panics.
     ///
     /// # Examples
     ///
@@ -1054,7 +1054,7 @@ impl Study<f64> {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Async objective function (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -1087,7 +1087,7 @@ impl Study<f64> {
                 .clone()
                 .acquire_owned()
                 .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?;
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?;
             let trial = self.create_trial_with_sampler();
             let objective = Arc::clone(&objective);
 
@@ -1104,7 +1104,7 @@ impl Study<f64> {
         for handle in handles {
             match handle
                 .await
-                .map_err(|e| crate::TpeError::TaskError(e.to_string()))?
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?
             {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
@@ -1117,7 +1117,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
diff --git a/src/trial.rs b/src/trial.rs
index b53b2d5..83c7a77 100644
--- a/src/trial.rs
+++ b/src/trial.rs
@@ -8,7 +8,7 @@ use parking_lot::RwLock;
 use crate::distribution::{
     CategoricalDistribution, Distribution, FloatDistribution, IntDistribution,
 };
-use crate::error::{Result, TpeError};
+use crate::error::{Error, Result};
 use crate::param::ParamValue;
 use crate::sampler::{CompletedTrial, Sampler};
 use crate::types::TrialState;
@@ -190,7 +190,7 @@ impl Trial {
     /// ```
     pub fn suggest_float(&mut self, name: impl Into<String>, low: f64, high: f64) -> Result<f64> {
         if low > high {
-            return Err(TpeError::InvalidBounds { low, high });
+            return Err(Error::InvalidBounds { low, high });
         }
 
         let name = name.into();
@@ -216,7 +216,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -226,7 +226,7 @@ impl Trial {
         // Sample using the sampler
         let dist = Distribution::Float(distribution);
         let ParamValue::Float(value) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
+            return Err(Error::Internal(
                 "Float distribution should return Float value",
             ));
         };
@@ -283,11 +283,11 @@ impl Trial {
         high: f64,
     ) -> Result<f64> {
         if low <= 0.0 {
-            return Err(TpeError::InvalidLogBounds);
+            return Err(Error::InvalidLogBounds);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds { low, high });
+            return Err(Error::InvalidBounds { low, high });
         }
 
         let name = name.into();
@@ -313,7 +313,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -323,7 +323,7 @@ impl Trial {
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Float(distribution);
         let ParamValue::Float(value) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
+            return Err(Error::Internal(
                 "Float distribution should return Float value",
             ));
         };
@@ -380,11 +380,11 @@ impl Trial {
         step: f64,
     ) -> Result<f64> {
         if step <= 0.0 {
-            return Err(TpeError::InvalidStep);
+            return Err(Error::InvalidStep);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds { low, high });
+            return Err(Error::InvalidBounds { low, high });
         }
 
         let name = name.into();
@@ -410,7 +410,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -420,7 +420,7 @@ impl Trial {
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Float(distribution);
         let ParamValue::Float(value) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
+            return Err(Error::Internal(
                 "Float distribution should return Float value",
             ));
         };
@@ -466,7 +466,7 @@ impl Trial {
     #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int(&mut self, name: impl Into<String>, low: i64, high: i64) -> Result<i64> {
         if low > high {
-            return Err(TpeError::InvalidBounds {
+            return Err(Error::InvalidBounds {
                 low: low as f64,
                 high: high as f64,
             });
@@ -495,7 +495,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -505,9 +505,7 @@ impl Trial {
         // Sample using the sampler
         let dist = Distribution::Int(distribution);
         let ParamValue::Int(value) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
-                "Int distribution should return Int value",
-            ));
+            return Err(Error::Internal("Int distribution should return Int value"));
         };
 
         // Store distribution and value
@@ -554,11 +552,11 @@ impl Trial {
     #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int_log(&mut self, name: impl Into<String>, low: i64, high: i64) -> Result<i64> {
         if low < 1 {
-            return Err(TpeError::InvalidLogBounds);
+            return Err(Error::InvalidLogBounds);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds {
+            return Err(Error::InvalidBounds {
                 low: low as f64,
                 high: high as f64,
             });
@@ -587,7 +585,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -597,9 +595,7 @@ impl Trial {
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Int(distribution);
         let ParamValue::Int(value) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
-                "Int distribution should return Int value",
-            ));
+            return Err(Error::Internal("Int distribution should return Int value"));
         };
 
         // Store distribution and value
@@ -659,11 +655,11 @@ impl Trial {
         step: i64,
     ) -> Result<i64> {
         if step <= 0 {
-            return Err(TpeError::InvalidStep);
+            return Err(Error::InvalidStep);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds {
+            return Err(Error::InvalidBounds {
                 low: low as f64,
                 high: high as f64,
             });
@@ -692,7 +688,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -702,9 +698,7 @@ impl Trial {
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Int(distribution);
         let ParamValue::Int(value) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
-                "Int distribution should return Int value",
-            ));
+            return Err(Error::Internal("Int distribution should return Int value"));
         };
 
         // Store distribution and value
@@ -760,7 +754,7 @@ impl Trial {
         choices: &[T],
     ) -> Result<T> {
         if choices.is_empty() {
-            return Err(TpeError::EmptyChoices);
+            return Err(Error::EmptyChoices);
         }
 
         let name = name.into();
@@ -779,7 +773,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different number of choices or type"
                     .to_string(),
@@ -789,7 +783,7 @@ impl Trial {
         // Sample using the sampler
         let dist = Distribution::Categorical(distribution);
         let ParamValue::Categorical(index) = self.sample_value(&dist) else {
-            return Err(TpeError::Internal(
+            return Err(Error::Internal(
                 "Categorical distribution should return Categorical value",
             ));
         };
diff --git a/tests/async_tests.rs b/tests/async_tests.rs
index d046baf..dae3862 100644
--- a/tests/async_tests.rs
+++ b/tests/async_tests.rs
@@ -6,7 +6,7 @@
 
 use optimizer::sampler::random::RandomSampler;
 use optimizer::sampler::tpe::TpeSampler;
-use optimizer::{Direction, Study, TpeError};
+use optimizer::{Direction, Error, Study};
 
 #[tokio::test]
 async fn test_optimize_async_basic() {
@@ -16,7 +16,7 @@ async fn test_optimize_async_basic() {
     study
         .optimize_async(10, |mut trial| async move {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x * x))
+            Ok::<_, Error>((trial, x * x))
         })
         .await
         .expect("async optimization should succeed");
@@ -39,7 +39,7 @@ async fn test_optimize_async_with_sampler() {
     study
         .optimize_async_with_sampler(15, |mut trial| async move {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>((trial, x * x))
+            Ok::<_, Error>((trial, x * x))
         })
         .await
         .expect("async optimization with sampler should succeed");
@@ -57,7 +57,7 @@ async fn test_optimize_parallel() {
     study
         .optimize_parallel(20, 4, |mut trial| async move {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x * x))
+            Ok::<_, Error>((trial, x * x))
         })
         .await
         .expect("parallel optimization should succeed");
@@ -79,7 +79,7 @@ async fn test_optimize_parallel_with_sampler() {
         .optimize_parallel_with_sampler(15, 3, |mut trial| async move {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
             let y = trial.suggest_float("y", -5.0, 5.0)?;
-            Ok::<_, TpeError>((trial, x * x + y * y))
+            Ok::<_, Error>((trial, x * x + y * y))
         })
         .await
         .expect("parallel optimization with sampler should succeed");
@@ -99,7 +99,7 @@ async fn test_optimize_async_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -116,7 +116,7 @@ async fn test_optimize_async_with_sampler_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -133,7 +133,7 @@ async fn test_optimize_parallel_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -150,7 +150,7 @@ async fn test_optimize_parallel_with_sampler_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -168,9 +168,9 @@ async fn test_optimize_async_partial_failures() {
             async move {
                 if count.is_multiple_of(2) {
                     let x = trial.suggest_float("x", 0.0, 10.0)?;
-                    Ok::<_, TpeError>((trial, x))
+                    Ok::<_, Error>((trial, x))
                 } else {
-                    Err(TpeError::NoCompletedTrials) // Use as error type
+                    Err(Error::NoCompletedTrials) // Use as error type
                 }
             }
         })
@@ -190,7 +190,7 @@ async fn test_optimize_parallel_high_concurrency() {
     study
         .optimize_parallel(5, 10, |mut trial| async move {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x))
+            Ok::<_, Error>((trial, x))
         })
         .await
         .expect("should handle high concurrency");
@@ -207,7 +207,7 @@ async fn test_optimize_parallel_single_concurrency() {
     study
         .optimize_parallel(10, 1, |mut trial| async move {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x))
+            Ok::<_, Error>((trial, x))
         })
         .await
         .expect("should work with single concurrency");
diff --git a/tests/integration.rs b/tests/integration.rs
index c576166..dd37f42 100644
--- a/tests/integration.rs
+++ b/tests/integration.rs
@@ -8,7 +8,7 @@
 
 use optimizer::sampler::random::RandomSampler;
 use optimizer::sampler::tpe::TpeSampler;
-use optimizer::{Direction, Study, TpeError, Trial};
+use optimizer::{Direction, Error, Study, Trial};
 
 // =============================================================================
 // Test: optimize simple quadratic function with TPE, finds near-optimal
@@ -30,7 +30,7 @@ fn test_tpe_optimizes_quadratic_function() {
     study
         .optimize_with_sampler(50, |trial| {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>((x - 3.0).powi(2))
+            Ok::<_, Error>((x - 3.0).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -61,7 +61,7 @@ fn test_tpe_optimizes_multivariate_function() {
         .optimize_with_sampler(100, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
             let y = trial.suggest_float("y", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x + y * y)
+            Ok::<_, Error>(x * x + y * y)
         })
         .expect("optimization should succeed");
 
@@ -90,7 +90,7 @@ fn test_tpe_maximization() {
     study
         .optimize_with_sampler(50, |trial| {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>(-(x - 2.0).powi(2) + 10.0)
+            Ok::<_, Error>(-(x - 2.0).powi(2) + 10.0)
         })
         .expect("optimization should succeed");
 
@@ -123,7 +123,7 @@ fn test_random_sampler_uniform_float_distribution() {
         .optimize(n_samples, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
             samples.push(x);
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -160,7 +160,7 @@ fn test_random_sampler_uniform_int_distribution() {
             let n = trial.suggest_int("n", 1, 10)?;
             assert!((1..=10).contains(&n), "sample {n} out of range [1, 10]");
             counts[(n - 1) as usize] += 1;
-            Ok::<_, TpeError>(n as f64)
+            Ok::<_, Error>(n as f64)
         })
         .unwrap();
 
@@ -192,7 +192,7 @@ fn test_random_sampler_uniform_categorical_distribution() {
             let choice = trial.suggest_categorical("cat", &choices)?;
             let idx = choices.iter().position(|&c| c == choice).unwrap();
             counts[idx] += 1;
-            Ok::<_, TpeError>(idx as f64)
+            Ok::<_, Error>(idx as f64)
         })
         .unwrap();
 
@@ -227,7 +227,7 @@ fn test_random_sampler_reproducibility() {
         .optimize_with_sampler(100, |trial| {
             let x = trial.suggest_float("x", 0.0, 100.0)?;
             values1.push(x);
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -235,7 +235,7 @@ fn test_random_sampler_reproducibility() {
         .optimize_with_sampler(100, |trial| {
             let x = trial.suggest_float("x", 0.0, 100.0)?;
             values2.push(x);
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -367,7 +367,7 @@ fn test_parameter_conflict_float_different_bounds() {
     trial.suggest_float("x", 0.0, 1.0).unwrap();
     let result = trial.suggest_float("x", 0.0, 2.0); // Different upper bound
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -377,7 +377,7 @@ fn test_parameter_conflict_float_vs_log() {
     trial.suggest_float("x", 0.1, 1.0).unwrap();
     let result = trial.suggest_float_log("x", 0.1, 1.0); // Same bounds but log scale
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -387,7 +387,7 @@ fn test_parameter_conflict_float_vs_step() {
     trial.suggest_float("x", 0.0, 1.0).unwrap();
     let result = trial.suggest_float_step("x", 0.0, 1.0, 0.1); // Same bounds but with step
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -397,7 +397,7 @@ fn test_parameter_conflict_int_different_bounds() {
     trial.suggest_int("n", 1, 10).unwrap();
     let result = trial.suggest_int("n", 1, 20); // Different upper bound
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -407,7 +407,7 @@ fn test_parameter_conflict_int_vs_log() {
     trial.suggest_int("n", 1, 100).unwrap();
     let result = trial.suggest_int_log("n", 1, 100); // Same bounds but log scale
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -417,7 +417,7 @@ fn test_parameter_conflict_categorical_different_n_choices() {
     trial.suggest_categorical("opt", &["a", "b", "c"]).unwrap();
     let result = trial.suggest_categorical("opt", &["x", "y"]); // Different number of choices
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -427,7 +427,7 @@ fn test_parameter_conflict_float_vs_int() {
     trial.suggest_float("x", 0.0, 10.0).unwrap();
     let result = trial.suggest_int("x", 0, 10); // Different type
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -438,7 +438,7 @@ fn test_parameter_conflict_returns_name() {
     let result = trial.suggest_float("my_param", 0.0, 2.0);
 
     match result {
-        Err(TpeError::ParameterConflict { name, .. }) => {
+        Err(Error::ParameterConflict { name, .. }) => {
             assert_eq!(name, "my_param");
         }
         _ => panic!("expected ParameterConflict error"),
@@ -456,7 +456,7 @@ fn test_empty_categorical_returns_error() {
 
     let result = trial.suggest_categorical("opt", empty);
 
-    assert!(matches!(result, Err(TpeError::EmptyChoices)));
+    assert!(matches!(result, Err(Error::EmptyChoices)));
 }
 
 #[test]
@@ -466,7 +466,7 @@ fn test_empty_categorical_vec_returns_error() {
 
     let result = trial.suggest_categorical("numbers", &empty);
 
-    assert!(matches!(result, Err(TpeError::EmptyChoices)));
+    assert!(matches!(result, Err(Error::EmptyChoices)));
 }
 
 // =============================================================================
@@ -480,7 +480,7 @@ fn test_study_basic_workflow() {
     study
         .optimize(10, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x)
+            Ok::<_, Error>(x * x)
         })
         .expect("optimization should succeed");
 
@@ -517,7 +517,7 @@ fn test_no_completed_trials_error() {
     let study: Study<f64> = Study::new(Direction::Minimize);
 
     let result = study.best_trial();
-    assert!(matches!(result, Err(TpeError::NoCompletedTrials)));
+    assert!(matches!(result, Err(Error::NoCompletedTrials)));
 }
 
 #[test]
@@ -526,11 +526,11 @@ fn test_invalid_bounds_errors() {
 
     // low > high for float
     let result = trial.suggest_float("x", 10.0, 5.0);
-    assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
+    assert!(matches!(result, Err(Error::InvalidBounds { .. })));
 
     // low > high for int
     let result = trial.suggest_int("n", 100, 50);
-    assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
+    assert!(matches!(result, Err(Error::InvalidBounds { .. })));
 }
 
 #[test]
@@ -539,14 +539,14 @@ fn test_invalid_log_bounds_errors() {
 
     // low <= 0 for log float
     let result = trial.suggest_float_log("x", 0.0, 1.0);
-    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
+    assert!(matches!(result, Err(Error::InvalidLogBounds)));
 
     let result = trial.suggest_float_log("y", -1.0, 1.0);
-    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
+    assert!(matches!(result, Err(Error::InvalidLogBounds)));
 
     // low < 1 for log int
     let result = trial.suggest_int_log("n", 0, 100);
-    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
+    assert!(matches!(result, Err(Error::InvalidLogBounds)));
 }
 
 #[test]
@@ -555,14 +555,14 @@ fn test_invalid_step_errors() {
 
     // step <= 0 for float
     let result = trial.suggest_float_step("x", 0.0, 1.0, 0.0);
-    assert!(matches!(result, Err(TpeError::InvalidStep)));
+    assert!(matches!(result, Err(Error::InvalidStep)));
 
     let result = trial.suggest_float_step("y", 0.0, 1.0, -0.1);
-    assert!(matches!(result, Err(TpeError::InvalidStep)));
+    assert!(matches!(result, Err(Error::InvalidStep)));
 
     // step <= 0 for int
     let result = trial.suggest_int_step("n", 0, 100, 0);
-    assert!(matches!(result, Err(TpeError::InvalidStep)));
+    assert!(matches!(result, Err(Error::InvalidStep)));
 }
 
 #[test]
@@ -588,7 +588,7 @@ fn test_tpe_with_categorical_parameter() {
                 "cubic" => -((x - 1.0).powi(2)) + 10.0, // peak at x=1, max value 10
                 _ => unreachable!(),
             };
-            Ok::<_, TpeError>(value)
+            Ok::<_, Error>(value)
         })
         .expect("optimization should succeed");
 
@@ -615,7 +615,7 @@ fn test_tpe_with_integer_parameters() {
     study
         .optimize_with_sampler(30, |trial| {
             let n = trial.suggest_int("n", 1, 10)?;
-            Ok::<_, TpeError>(((n - 7) as f64).powi(2))
+            Ok::<_, Error>(((n - 7) as f64).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -643,7 +643,7 @@ fn test_callback_early_stopping() {
             |trial| {
                 trials_run.set(trials_run.get() + 1);
                 let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
+                Ok::<_, Error>(x)
             },
             |_study, _trial| {
                 // Stop after 5 trials
@@ -666,7 +666,7 @@ fn test_study_trials_iteration() {
     study
         .optimize(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -758,7 +758,7 @@ fn test_best_value() {
     study
         .optimize(10, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -792,7 +792,7 @@ fn test_study_set_sampler() {
     study
         .optimize_with_sampler(10, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x)
+            Ok::<_, Error>(x * x)
         })
         .expect("optimization should succeed with new sampler");
 
@@ -807,7 +807,7 @@ fn test_study_with_i32_value_type() {
     study
         .optimize(10, |trial| {
             let x = trial.suggest_int("x", -10, 10)?;
-            Ok::<_, TpeError>(x.abs() as i32)
+            Ok::<_, Error>(x.abs() as i32)
         })
         .expect("optimization should succeed");
 
@@ -824,7 +824,7 @@ fn test_optimize_all_trials_fail() {
     let result = study.optimize(5, |_trial| Err::<f64, &str>("always fails"));
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -842,7 +842,7 @@ fn test_optimize_with_callback_all_trials_fail() {
     );
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -854,7 +854,7 @@ fn test_optimize_with_sampler_all_trials_fail() {
     let result = study.optimize_with_sampler(5, |_trial| Err::<f64, &str>("always fails"));
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -872,7 +872,7 @@ fn test_optimize_with_callback_sampler_all_trials_fail() {
     );
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -902,7 +902,7 @@ fn test_tpe_sampler_builder_default_trait() {
     study
         .optimize_with_sampler(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -917,7 +917,7 @@ fn test_tpe_sampler_default_trait() {
     study
         .optimize_with_sampler(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -938,7 +938,7 @@ fn test_tpe_with_fixed_kde_bandwidth() {
     study
         .optimize_with_sampler(20, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x)
+            Ok::<_, Error>(x * x)
         })
         .expect("optimization should succeed");
 
@@ -949,7 +949,7 @@ fn test_tpe_with_fixed_kde_bandwidth() {
 #[test]
 fn test_tpe_sampler_invalid_kde_bandwidth() {
     let result = TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
-    assert!(matches!(result, Err(TpeError::InvalidBandwidth(_))));
+    assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
 }
 
 #[test]
@@ -966,7 +966,7 @@ fn test_tpe_split_trials_with_two_trials() {
     study
         .optimize_with_sampler(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .expect("optimization should succeed with small history");
 
@@ -987,7 +987,7 @@ fn test_tpe_with_log_scale_int() {
         .optimize_with_sampler(20, |trial| {
             let batch_size = trial.suggest_int_log("batch_size", 1, 1024)?;
             // Optimal around batch_size = 32
-            Ok::<_, TpeError>(((batch_size as f64).log2() - 5.0).powi(2))
+            Ok::<_, Error>(((batch_size as f64).log2() - 5.0).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -1009,7 +1009,7 @@ fn test_tpe_with_step_distributions() {
         .optimize_with_sampler(20, |trial| {
             let x = trial.suggest_float_step("x", 0.0, 10.0, 0.5)?;
             let n = trial.suggest_int_step("n", 0, 100, 10)?;
-            Ok::<_, TpeError>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
+            Ok::<_, Error>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -1088,7 +1088,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
     study
         .optimize_with_sampler(10, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -1096,7 +1096,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
     study
         .optimize_with_sampler(5, |trial| {
             let y = trial.suggest_float("y", 0.0, 10.0)?;
-            Ok::<_, TpeError>(y)
+            Ok::<_, Error>(y)
         })
         .unwrap();
 
@@ -1114,7 +1114,7 @@ fn test_callback_early_stopping_on_first_trial() {
             100,
             |trial| {
                 let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
+                Ok::<_, Error>(x)
             },
             |_study, _trial| {
                 // Stop immediately after first trial
@@ -1138,7 +1138,7 @@ fn test_callback_sampler_early_stopping() {
             100,
             |trial| {
                 let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
+                Ok::<_, Error>(x)
             },
             |study, _trial| {
                 if study.n_trials() >= 3 {
@@ -1174,7 +1174,7 @@ fn test_best_trial_with_nan_values() {
     study
         .optimize(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();