---

name: ds-bayesian-inference compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Applies Bayesian methods for prior selection, posterior estimation and probabilistic inference in machine learning models"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-confidence-intervals, ds-hypothesis-testing, ds-maximum-likelihood ds-monte-carlo ds-monte-carlo role: implementation scope: implementation triggers: bayesian inference, bayes, prior, posterior, probabilistic inference how do i do bayesian archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

---

Bayesian Inference

Comprehensive guide to bayesian inference in machine learning and data science workflows.

When to Use This Skill

• Solving real-world statistical inference problems
• Building machine learning pipelines with bayesian inference
• Implementing best practices for bayesian inference
• Optimizing model performance using bayesian inference techniques
• Learning industry-standard approaches to bayesian inference

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require bayesian inference rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Bayesian Inference is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Bayesian Inference

import numpy as np
import pandas as pd
from scipy import stats
from typing import Tuple, Dict

def bayesian_linear_regression(
    X: np.ndarray, 
    y: np.ndarray, 
    prior_precision: float = 1.0
) -> Dict[str, np.ndarray]:
    """
    Perform Bayesian Linear Regression using conjugate Normal-Gamma priors.
    Computes posterior distribution over regression weights analytically.
    
    Args:
        X: Feature matrix of shape (n_samples, n_features)
        y: Target vector of shape (n_samples,)
        prior_precision: Precision (inverse variance) of the prior on weights
        
    Returns:
        Dictionary containing posterior mean, covariance, and noise variance
    """
    if X.ndim == 1:
        X = X.reshape(-1, 1)
        
    n_samples, n_features = X.shape
    X_b = np.column_stack([np.ones(n_samples), X])
    
    # Initialize prior covariance matrix
    prior_cov = np.eye(n_features + 1) / prior_precision
    
    # Estimate initial noise variance from data
    beta_mle = np.linalg.lstsq(X_b, y, rcond=None)[0]
    residuals = y - X_b @ beta_mle
    sigma2_init = np.sum(residuals**2) / n_samples
    
    # Compute posterior precision and covariance
    posterior_precision = np.linalg.inv(prior_cov) + (1.0 / sigma2_init) * (X_b.T @ X_b)
    posterior_cov = np.linalg.inv(posterior_precision)
    
    # Compute posterior mean
    posterior_mean = posterior_cov @ (
        np.linalg.inv(prior_cov) @ np.zeros(n_features + 1) + 
        (1.0 / sigma2_init) * (X_b.T @ y)
    )
    
    return {
        'posterior_mean': posterior_mean
        'posterior_cov': posterior_cov
        'noise_variance': sigma2_init
        'n_samples': n_samples
        'n_features': n_features
    }

Pattern 2: Production-Ready Bayesian Inference

import logging
import numpy as np
import pandas as pd
from typing import Any, Dict, Optional
from scipy import stats

logger = logging.getLogger(__name__)

class BayesianInferenceEngine:
    """Production-grade Bayesian Inference engine for regression tasks."""
    
    def __init__(self, prior_precision: float = 1.0, confidence_level: float = 0.95) -> None:
        self.prior_precision = prior_precision
        self.confidence_level = confidence_level
        logger.info("BayesianInferenceEngine initialized with prior_precision=%.2f, confidence=%.2f", 
                    prior_precision, confidence_level)
    
    def _validate_input(self, data: pd.DataFrame) -> None:
        if data is None or data.empty:
            raise ValueError("Input data cannot be None or empty")
        if not isinstance(data, pd.DataFrame):
            raise TypeError("Input must be a pandas DataFrame")
        if data.shape[0] < 2:
            raise ValueError("Insufficient data for inference: requires at least 2 samples")
    
    def execute(self, data: pd.DataFrame, target_col: str = 'y') -> Dict[str, Any]:
        """Execute Bayesian inference on provided data and return structured results."""
        self._validate_input(data)
        logger.info("Running Bayesian inference on %d samples", len(data))
        
        X = data.drop(columns=[target_col]).values
        y = data[target_col].values
        
        n, d = X.shape
        X_b = np.column_stack([np.ones(n), X])
        
        prior_cov = np.eye(d + 1) / self.prior_precision
        sigma2 = np.var(y - np.mean(y))
        
        post_prec = np.linalg.inv(prior_cov) + (1.0 / sigma2) * (X_b.T @ X_b)
        post_cov = np.linalg.inv(post_prec)
        post_mean = post_cov @ (np.linalg.inv(prior_cov) @ np.zeros(d + 1) + 
                                (1.0 / sigma2) * (X_b.T @ y))
        
        y_pred = X_b @ post_mean
        pred_var = sigma2 * (1 + np.sum(X_b * (post_cov @ X_b.T), axis=1))
        ci_half_width = stats.norm.ppf((1 + self.confidence_level) / 2) * np.sqrt(pred_var)
        
        return {
            'posterior_mean': post_mean
            'posterior_cov': post_cov
            'predictions': y_pred
            'lower_bound': y_pred - ci_half_width
            'upper_bound': y_pred + ci_half_width
            'noise_variance': sigma2
            'status': 'success'
        }

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• PyMC Documentation
• Bayesian Inference — Wikipedia
• Bayesian Methods for Hackers
• Stan User's Guide
• Bayesian A/B Testing (Optimizely), |
• Statistical Rethinking — Richard McElreath