--- name: bayesian-statistics-guide-ee2e7e44a134 description: "Bayesian inference methods including prior selection, MCMC, and model comparison" metadata: openclaw: emoji: "📐" category: "analysis" subcategory: "statistics" keywords: ["Bayesian statistics", "Bayesian inference", "MCMC", "sample size calculation", "prior selection"] source: "wentor" --- # Bayesian Statistics Guide A skill for applying Bayesian statistical methods to research data analysis. Covers prior specification, Markov chain Monte Carlo (MCMC) sampling, posterior interpretation, model comparison, and reporting standards. ## Bayesian Framework Overview ### Bayes' Theorem in Practice ``` Posterior = (Likelihood x Prior) / Evidence P(theta | data) = P(data | theta) * P(theta) / P(data) In practice: P(theta | data) is proportional to P(data | theta) * P(theta) (the denominator is a normalizing constant) ``` ### When to Use Bayesian Methods | Scenario | Bayesian Advantage | |----------|-------------------| | Small sample sizes | Priors regularize estimates | | Complex hierarchical models | Natural framework for multilevel data | | Sequential data collection | Update beliefs as data arrives | | Prior knowledge available | Formally incorporate existing evidence | | Model comparison | Bayes factors and posterior model probabilities | | Prediction | Full posterior predictive distributions | ## Prior Specification ### Types of Priors ```python import numpy as np from scipy import stats import matplotlib.pyplot as plt def visualize_priors(parameter_name: str, prior_type: str = 'weakly_informative'): """ Visualize common prior choices for a parameter. """ x = np.linspace(-10, 10, 1000) priors = { 'flat': { 'dist': stats.uniform(loc=-100, scale=200), 'description': 'Flat/Uniform: minimal prior info (often improper)', 'recommendation': 'Avoid -- can lead to improper posteriors' }, 'weakly_informative': { 'dist': stats.norm(loc=0, scale=2.5), 'description': 'Weakly informative: Normal(0, 2.5)', 'recommendation': 'Good default for regression coefficients' }, 'informative': { 'dist': stats.norm(loc=0.5, scale=0.2), 'description': 'Informative: based on previous studies', 'recommendation': 'Use when strong prior evidence exists' }, 'horseshoe': { 'dist': stats.cauchy(loc=0, scale=1), 'description': 'Horseshoe-like (Cauchy): sparsity-inducing', 'recommendation': 'Good for variable selection problems' } } prior = priors.get(prior_type, priors['weakly_informative']) return prior # Recommended default priors (Gelman et al., 2008): # Intercept: Normal(0, 10) # Coefficients: Normal(0, 2.5) on standardized predictors # Standard deviation: Half-Cauchy(0, 2.5) or Exponential(1) # Correlation: LKJ(2) for correlation matrices ``` ## MCMC with PyMC ### Linear Regression Example ```python import pymc as pm import arviz as az def bayesian_regression(X, y, feature_names=None): """ Fit a Bayesian linear regression model using PyMC. Args: X: Feature matrix (n_samples, n_features) y: Response variable (n_samples,) feature_names: List of feature names """ n_features = X.shape[1] if feature_names is None: feature_names = [f'x{i}' for i in range(n_features)] with pm.Model() as model: # Priors intercept = pm.Normal('intercept', mu=0, sigma=10) betas = pm.Normal('betas', mu=0, sigma=2.5, shape=n_features) sigma = pm.HalfCauchy('sigma', beta=2.5) # Linear predictor mu = intercept + pm.math.dot(X, betas) # Likelihood y_obs = pm.Normal('y_obs', mu=mu, sigma=sigma, observed=y) # MCMC sampling trace = pm.sample( draws=2000, tune=1000, chains=4, cores=4, target_accept=0.9, return_inferencedata=True ) return model, trace # After fitting, analyze results: # az.summary(trace, var_names=['intercept', 'betas', 'sigma']) # az.plot_trace(trace) # az.plot_forest(trace, var_names=['betas']) ``` ## Diagnostics ### MCMC Convergence Checks ```python def check_mcmc_diagnostics(trace) -> dict: """ Check MCMC convergence diagnostics. """ summary = az.summary(trace) diagnostics = { 'r_hat': { 'values': summary['r_hat'].to_dict(), 'threshold': 1.01, 'pass': (summary['r_hat'] < 1.01).all(), 'interpretation': 'R-hat < 1.01 indicates convergence' }, 'ess_bulk': { 'min_value': summary['ess_bulk'].min(), 'threshold': 400, 'pass': (summary['ess_bulk'] > 400).all(), 'interpretation': 'ESS > 400 ensures reliable posterior estimates' }, 'ess_tail': { 'min_value': summary['ess_tail'].min(), 'threshold': 400, 'pass': (summary['ess_tail'] > 400).all(), 'interpretation': 'Tail ESS > 400 ensures reliable credible intervals' } } # Overall assessment diagnostics['converged'] = all( d['pass'] for d in diagnostics.values() if 'pass' in d ) return diagnostics ``` ## Model Comparison ### Bayesian Model Selection ```python def compare_models(traces: dict) -> dict: """ Compare Bayesian models using LOO-CV and WAIC. Args: traces: Dict mapping model names to InferenceData objects """ comparison = az.compare(traces, ic='loo') return { 'ranking': comparison.index.tolist(), 'loo_values': comparison['loo'].to_dict(), 'weights': comparison['weight'].to_dict(), 'interpretation': ( f"Best model: {comparison.index[0]} " f"(weight = {comparison['weight'].iloc[0]:.2f})" ) } ``` ## Reporting Bayesian Results Follow the WAMBS checklist (Depaoli & van de Schoot, 2017): 1. **Priors**: Report all prior distributions and justify choices 2. **Convergence**: Report R-hat, ESS, and trace plots (in supplement) 3. **Posteriors**: Report posterior mean/median, 95% credible interval (HDI preferred) 4. **Sensitivity**: Show results are robust to reasonable prior changes 5. **Model fit**: Report LOO-IC, WAIC, or posterior predictive checks Example results sentence: "The effect of treatment on outcome was estimated at beta = 0.45, 95% HDI [0.21, 0.68], with a posterior probability of 0.99 that the effect is positive." ## References - Gelman, A., et al. (2013). *Bayesian Data Analysis* (3rd ed.). CRC Press. - McElreath, R. (2020). *Statistical Rethinking* (2nd ed.). 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