pymc-bayesian-modeling

name	pymc-bayesian-modeling
description	Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.
license	Apache-2.0

PyMC Bayesian Modeling

Overview

PyMC is a Python library for Bayesian statistical modeling and probabilistic programming. It provides an expressive syntax for defining probabilistic models and efficient inference via MCMC (NUTS) and variational methods (ADVI). This skill covers the full Bayesian modeling cycle from model specification through diagnostics, comparison, and prediction.

When to Use

• Estimating parameters with full uncertainty quantification (credible intervals, not just point estimates)
• Fitting hierarchical/multilevel models to grouped or nested data
• Performing prior and posterior predictive checks to validate model assumptions
• Comparing candidate models using information criteria (LOO-CV, WAIC)
• Building regression models (linear, logistic, Poisson) in a Bayesian framework
• Handling missing data or measurement error as latent parameters
• Modeling time series with autoregressive or random walk priors
• Generating posterior predictions for new observations with uncertainty bounds
• Use Stan/PyStan instead for compiled, more scalable Bayesian inference on large models; use statsmodels for frequentist statistical tests

Prerequisites

• Python packages: pymc >= 5.0, arviz, numpy, matplotlib
• Data: NumPy arrays or pandas DataFrames with numeric columns
• Environment: CPU sufficient for most models; GPU via JAX backend for large models

pip install pymc arviz numpy matplotlib
# Optional: JAX backend for GPU acceleration
pip install pymc[jax]

Quick Start

import pymc as pm
import arviz as az
import numpy as np

# Simulate data
np.random.seed(42)
X = np.random.randn(100)
y = 2.5 + 1.3 * X + np.random.randn(100) * 0.5

# Build and fit model
with pm.Model() as model:
    alpha = pm.Normal("alpha", mu=0, sigma=5)
    beta = pm.Normal("beta", mu=0, sigma=5)
    sigma = pm.HalfNormal("sigma", sigma=1)
    mu = alpha + beta * X
    y_obs = pm.Normal("y_obs", mu=mu, sigma=sigma, observed=y)
    idata = pm.sample(1000, tune=1000, chains=4, random_seed=42)

print(az.summary(idata, var_names=["alpha", "beta", "sigma"]))
# Expected: alpha ~ 2.5, beta ~ 1.3, sigma ~ 0.5

Workflow

Step 1: Prepare Data

Standardize continuous predictors for better sampling efficiency. Use named coordinates for readable models and ArviZ integration.

import pymc as pm
import arviz as az
import numpy as np

# Load data
X = np.random.randn(200, 3)  # 200 obs, 3 predictors
y = X @ np.array([1.0, -0.5, 0.3]) + np.random.randn(200) * 0.8

# Standardize predictors
X_mean, X_std = X.mean(axis=0), X.std(axis=0)
X_scaled = (X - X_mean) / X_std

# Define coordinates for named dimensions
coords = {
    "predictors": ["var1", "var2", "var3"],
    "obs_id": np.arange(len(y)),
}
print(f"Data shape: X={X_scaled.shape}, y={y.shape}")

Step 2: Define Model and Set Priors

Specify the model structure inside a pm.Model() context. Use weakly informative priors, dims for named dimensions, and HalfNormal or Exponential for scale parameters.

with pm.Model(coords=coords) as model:
    # Priors — weakly informative, not flat
    alpha = pm.Normal("alpha", mu=0, sigma=1)
    beta = pm.Normal("beta", mu=0, sigma=1, dims="predictors")
    sigma = pm.HalfNormal("sigma", sigma=1)

    # Linear predictor
    mu = alpha + pm.math.dot(X_scaled, beta)

    # Likelihood
    y_obs = pm.Normal("y_obs", mu=mu, sigma=sigma, observed=y, dims="obs_id")

# Inspect model variables
print(model.basic_RVs)  # Lists: [alpha, beta, sigma, y_obs]

Step 3: Prior Predictive Check

Validate that priors produce plausible data ranges before fitting. Adjust priors if simulated data is unreasonable.

with model:
    prior_pred = pm.sample_prior_predictive(samples=1000, random_seed=42)

# Check prior-implied data range
prior_y = prior_pred.prior_predictive["y_obs"].values.flatten()
print(f"Prior predictive range: [{prior_y.min():.1f}, {prior_y.max():.1f}]")
print(f"Observed data range:    [{y.min():.1f}, {y.max():.1f}]")

az.plot_ppc(prior_pred, group="prior", num_pp_samples=100)

Step 4: Sample Posterior (MCMC)

Run NUTS sampling with multiple chains. Include log_likelihood=True if you plan model comparison later.

with model:
    idata = pm.sample(
        draws=2000,
        tune=1000,
        chains=4,
        target_accept=0.9,
        random_seed=42,
        idata_kwargs={"log_likelihood": True},
    )

print(f"Posterior shape: {idata.posterior['beta'].shape}")
# Expected: (4 chains, 2000 draws, 3 predictors)

Step 5: Diagnose Sampling

Check convergence before interpreting results. All three diagnostics (R-hat, ESS, divergences) must pass.

# Summary with convergence diagnostics
summary = az.summary(idata, var_names=["alpha", "beta", "sigma"])
print(summary[["mean", "sd", "hdi_3%", "hdi_97%", "r_hat", "ess_bulk"]])

# R-hat convergence check
bad_rhat = summary[summary["r_hat"] > 1.01]
if len(bad_rhat) > 0:
    print(f"WARNING: {len(bad_rhat)} parameters with R-hat > 1.01")
    print(bad_rhat[["r_hat"]])

# Effective sample size check
low_ess = summary[summary["ess_bulk"] < 400]
if len(low_ess) > 0:
    print(f"WARNING: {len(low_ess)} parameters with ESS < 400")

# Divergence check
n_div = idata.sample_stats.diverging.sum().item()
total = len(idata.posterior.draw) * len(idata.posterior.chain)
print(f"Divergences: {n_div}/{total} ({n_div / total * 100:.2f}%)")

# Visual diagnostics — trace plots and rank plots
az.plot_trace(idata, var_names=["alpha", "beta", "sigma"])
az.plot_rank(idata, var_names=["alpha", "beta", "sigma"])

Step 6: Posterior Predictive Check

Validate model fit by comparing simulated data from the posterior to observed data.

with model:
    pm.sample_posterior_predictive(idata, extend_inferencedata=True, random_seed=42)

az.plot_ppc(idata, num_pp_samples=100)
# Blue = observed data, grey = posterior simulations
# Systematic deviations indicate model misspecification

Step 7: Compare Models

Use LOO-CV or WAIC to compare candidate models. Lower information criterion is better.

# Fit multiple models with log_likelihood=True, then compare
# Example: compare linear vs a second model
idatas = {"linear": idata}  # add more fitted models here

comparison = az.compare(idatas, ic="loo")
print(comparison[["rank", "elpd_loo", "p_loo", "d_loo", "weight"]])

# Check LOO reliability via Pareto-k diagnostics
loo_result = az.loo(idata, pointwise=True)
high_k = (loo_result.pareto_k > 0.7).sum().item()
print(f"Observations with Pareto-k > 0.7: {high_k}")
# Interpretation: Dloo < 2 = similar models; Dloo > 10 = strong evidence

az.plot_compare(comparison)

Step 8: Generate Predictions

Produce posterior predictions for new data with full uncertainty propagation.

X_new = np.array([[0.5, -1.0, 0.2]])
X_new_scaled = (X_new - X_mean) / X_std

with model:
    pm.set_data({"X_scaled": X_new_scaled})
    post_pred = pm.sample_posterior_predictive(
        idata.posterior, var_names=["y_obs"], random_seed=42
    )

y_pred = post_pred.posterior_predictive["y_obs"]
print(f"Predicted mean: {y_pred.mean().item():.3f}")
print(f"94% HDI: {az.hdi(y_pred, hdi_prob=0.94).values}")

Key Parameters

Parameter	Default	Range / Options	Effect
draws	1000	500-10000	Number of posterior samples per chain
tune	1000	500-5000	Warmup iterations (discarded); increase for complex posteriors
chains	4	2-8	Number of independent chains; minimum 4 for reliable R-hat
cores	all CPUs	1-N	Parallel chains; set equal to chains for full parallelism
target_accept	0.8	0.8-0.99	NUTS acceptance rate; increase to reduce divergences
init	"auto"	"adapt_diag", "jitter+adapt_diag", "advi"	Initialization strategy for sampler
random_seed	None	any int	Seed for reproducibility
idata_kwargs	{}	{"log_likelihood": True}	Store log-likelihood for LOO/WAIC model comparison
method (pm.fit)	"advi"	"advi", "fullrank_advi", "svgd"	Variational inference algorithm
n (pm.fit)	10000	5000-100000	VI optimization iterations
samples (prior pred)	500	100-5000	Prior predictive samples for validation

Key Concepts

Prior/Distribution Selection Guide

Distribution	Use When	Key Parameters
Normal(mu, sigma)	Unbounded real-valued parameter (standardized data)	mu: center, sigma: spread
HalfNormal(sigma)	Scale/standard deviation parameter (positive)	sigma: spread of positive half
Exponential(lam)	Scale parameter, alternative to HalfNormal	lam: rate (1/mean)
StudentT(nu, mu, sigma)	Robust alternative to Normal (outlier-resistant)	nu: degrees of freedom (<10 = heavier tails)
Beta(alpha, beta)	Probability or proportion in [0,1]	alpha=beta=2: weakly informative
Gamma(alpha, beta)	Positive parameter (rate, concentration)	alpha: shape, beta: rate
LogNormal(mu, sigma)	Positive parameter with multiplicative effects	mu, sigma: of underlying Normal
LKJCorr(n, eta)	Correlation matrix prior	eta=1: uniform; eta>1: prefer identity
Dirichlet(a)	Probability vector (sums to 1)	a: concentration; uniform if all equal
Bernoulli(p / logit_p)	Binary outcome likelihood	Use logit_p for numerical stability
Poisson(mu)	Count data (equidispersed)	mu: rate; use NegBinomial if overdispersed
NegativeBinomial(mu, alpha)	Overdispersed count data	alpha: dispersion (smaller = more overdispersion)

Diagnostic Thresholds

Metric	Threshold	Interpretation	Action if Failed
R-hat	< 1.01	Chains converged	Run longer chains; check multimodality
ESS bulk	> 400	Sufficient independent samples	Increase draws; reparameterize
ESS tail	> 400	Reliable tail estimates	Increase draws
Divergences	0	NUTS explored successfully	Increase target_accept; non-centered param.
Pareto-k (LOO)	< 0.7	LOO estimate reliable	Use WAIC or k-fold CV
Max tree depth	< 10	No trajectory truncation	Reparameterize or increase max_treedepth

Model Variants Overview

Problem Type	Recipe	Likelihood	Key Feature
Grouped/nested data	Hierarchical Model	Normal (varies)	Non-centered parameterization, partial pooling
Binary outcome	Logistic Regression	Bernoulli	logit_p link function
Nonlinear/spatial	Gaussian Process	Normal	Kernel-based covariance, flexible shape
Count data	(use Poisson in Workflow)	Poisson / NegBinomial	Log link; NegBinomial for overdispersion
Time series	(see references)	AR / GaussianRandomWalk	Autoregressive coefficients
Mixture/clustering	(see references)	Mixture / NormalMixture	Component weights via Dirichlet

Common Recipes

Recipe: Hierarchical Model

When to use: data has natural grouping (patients within hospitals, students within schools). Non-centered parameterization avoids divergences from funnel geometry.

import pymc as pm
import arviz as az
import numpy as np

n_groups, n_per_group = 5, 30
group_idx = np.repeat(np.arange(n_groups), n_per_group)
group_names = [f"group_{i}" for i in range(n_groups)]

# Simulated grouped data
true_alphas = np.random.normal(3.0, 1.5, n_groups)
y_obs = np.random.normal(true_alphas[group_idx], 0.5)

with pm.Model(coords={"groups": group_names}) as hierarchical_model:
    # Hyperpriors (population level)
    mu_alpha = pm.Normal("mu_alpha", mu=0, sigma=5)
    sigma_alpha = pm.HalfNormal("sigma_alpha", sigma=2)

    # Non-centered parameterization
    alpha_offset = pm.Normal("alpha_offset", mu=0, sigma=1, dims="groups")
    alpha = pm.Deterministic(
        "alpha", mu_alpha + sigma_alpha * alpha_offset, dims="groups"
    )

    # Observation level
    sigma = pm.HalfNormal("sigma", sigma=1)
    y = pm.Normal("y", mu=alpha[group_idx], sigma=sigma, observed=y_obs)

    idata_hier = pm.sample(2000, tune=1000, target_accept=0.95, random_seed=42)

print(az.summary(idata_hier, var_names=["mu_alpha", "sigma_alpha", "alpha", "sigma"]))

Recipe: Logistic Regression

When to use: binary outcome variable (success/failure, disease/healthy). Use logit_p for numerical stability instead of computing probabilities directly.

import pymc as pm
import arviz as az
import numpy as np

# Simulated binary outcome
np.random.seed(42)
n, p = 200, 3
X_lr = np.random.randn(n, p)
true_beta = np.array([1.0, -0.5, 0.3])
prob = 1 / (1 + np.exp(-(0.2 + X_lr @ true_beta)))
y_binary = np.random.binomial(1, prob)

with pm.Model() as logistic_model:
    alpha = pm.Normal("alpha", mu=0, sigma=2)
    beta = pm.Normal("beta", mu=0, sigma=2, shape=p)
    logit_p = alpha + pm.math.dot(X_lr, beta)
    y = pm.Bernoulli("y", logit_p=logit_p, observed=y_binary)

    idata_logit = pm.sample(2000, tune=1000, target_accept=0.9, random_seed=42)

summary = az.summary(idata_logit, var_names=["alpha", "beta"])
print(summary[["mean", "sd", "hdi_3%", "hdi_97%"]])
# Odds ratios
beta_samples = idata_logit.posterior["beta"].values.reshape(-1, p)
print(f"Odds ratios (median): {np.median(np.exp(beta_samples), axis=0)}")

Recipe: Gaussian Process

When to use: modeling unknown nonlinear functions, spatial data, or smooth latent processes. Computationally expensive for n > 1000; consider sparse approximations for larger datasets.

import pymc as pm
import arviz as az
import numpy as np

# Simulated nonlinear data
np.random.seed(42)
X_gp = np.sort(np.random.uniform(0, 10, 60))[:, None]
y_gp = np.sin(X_gp[:, 0]) + np.random.randn(60) * 0.3

with pm.Model() as gp_model:
    # GP hyperparameters
    ls = pm.Gamma("lengthscale", alpha=2, beta=1)
    eta = pm.HalfNormal("amplitude", sigma=2)
    sigma_noise = pm.HalfNormal("noise", sigma=0.5)

    # Covariance function
    cov = eta**2 * pm.gp.cov.Matern52(1, ls=ls)
    gp = pm.gp.Marginal(cov_func=cov)

    # Marginal likelihood
    y_ = gp.marginal_likelihood("y", X=X_gp, y=y_gp, sigma=sigma_noise)
    idata_gp = pm.sample(1000, tune=1000, target_accept=0.9, random_seed=42)

# Predict on new points
X_new_gp = np.linspace(0, 10, 100)[:, None]
with gp_model:
    f_pred = gp.conditional("f_pred", X_new_gp)
    pred_samples = pm.sample_posterior_predictive(idata_gp, var_names=["f_pred"])

print(f"GP predictions shape: {pred_samples.posterior_predictive['f_pred'].shape}")

Expected Outputs

• Trace plots (az.plot_trace) -- chain mixing visualization; healthy chains look like "fuzzy caterpillars"
• Posterior summary (az.summary) -- mean, SD, HDI, R-hat, ESS per parameter
• Prior/posterior predictive plots (az.plot_ppc) -- simulated vs observed data distributions
• Forest plots (az.plot_forest) -- coefficient estimates with credible intervals
• Model comparison table (az.compare) -- ranked models with LOO/WAIC, weights, warnings
• Rank plots (az.plot_rank) -- uniform histograms indicate good mixing
• Energy plot (az.plot_energy) -- HMC energy transition diagnostics
• InferenceData file (idata.to_netcdf("results.nc")) -- serialized results for later analysis

Troubleshooting

Problem	Cause	Solution
Divergent transitions	Posterior geometry difficult for NUTS (funnels, ridges)	Increase target_accept=0.95; use non-centered parameterization; add stronger priors
Low ESS (< 400)	High autocorrelation between samples	Increase draws=5000; reparameterize to reduce correlation; try QR decomposition for correlated predictors
R-hat > 1.01	Chains have not converged	Increase tune=2000, draws=5000; check for multimodality; initialize with ADVI
Slow sampling	Complex posterior or large dataset	Use ADVI for initialization; reduce model complexity; increase cores; try JAX backend
Pareto-k > 0.7 in LOO	Influential observations affecting LOO estimate	Use WAIC instead; investigate influential points; consider k-fold CV
Hit max tree depth	Model geometry requires long trajectories	Reparameterize model; increase max_treedepth parameter
SamplingError at start	Poor initialization	Try init="adapt_diag" or init="jitter+adapt_diag"; provide initvals via MAP
Biased posterior (prior dominates)	Priors too strong relative to data	Weaken priors (increase sigma); check prior predictive; verify data is correctly passed
ValueError: logp = -inf	Parameter hit impossible region	Check data for NaN/Inf; ensure positive params use HalfNormal/Gamma; verify likelihood matches data type

Best Practices

1. Use weakly informative priors, not flat priors. Flat priors (Uniform over large ranges) cause sampling difficulties and are rarely appropriate.
2. Always run prior predictive checks before fitting. If prior-implied data is implausible, adjust priors before sampling.
3. Standardize continuous predictors to mean=0, sd=1 for better NUTS geometry and faster sampling.
4. Use non-centered parameterization for hierarchical models: offset ~ Normal(0,1); param = mu + sigma * offset instead of param ~ Normal(mu, sigma).
5. Run at least 4 chains for reliable R-hat and ESS diagnostics. Never trust a single chain.
6. Check all three diagnostics (R-hat < 1.01, ESS > 400, 0 divergences) before interpreting posteriors.
7. Include log_likelihood=True when fitting if you plan to compare models via LOO or WAIC.
8. Use dims instead of shape for named dimensions -- integrates with ArviZ for labeled summaries and subsetting.
9. Report credible intervals (HDI), not just point estimates. The posterior distribution is the result, not a single number.
10. Save results with idata.to_netcdf("results.nc") for reproducibility and later re-analysis.

Bundled Resources

Two reference files provide deeper detail for on-demand consultation:

• references/distributions_inference.md -- Distribution catalog organized by category (continuous, discrete, multivariate, mixture, time series, special/modifiers) with full parameter signatures, support, and common uses. Sampling and inference methods (NUTS, Metropolis, Slice, CompoundStep, SMC, ADVI, fullrank ADVI, SVGD, MAP) with code examples and a method selection guide table. Reparameterization tricks (non-centered, QR decomposition) with code. Covers: consolidated from original references/distributions.md (320 lines) and references/sampling_inference.md (424 lines). Relocated inline: core distribution selection guide table (Key Concepts), diagnostic threshold table (Key Concepts), basic ADVI usage (Recipe: Variational Inference in original, covered in references here). Omitted: shape broadcasting examples (trivial NumPy-style shape=5), basic dims usage (covered in Workflow Step 2), prior predictive sampling (covered in Workflow Step 3).

• references/advanced_workflows.md -- Model comparison workflow (LOO, WAIC, Pareto-k reliability checks, model averaging with weighted predictions), comprehensive diagnostic report generation (trace plots, rank plots, autocorrelation, energy, ESS evolution), prior-posterior comparison patterns, data preparation best practices (standardization, centering, missing data imputation), prior selection guidelines (weakly informative vs informative with domain knowledge), named dimensions (dims) usage with xarray subsetting, save/load patterns (NetCDF, pickle). Mixture model pattern. Covers: consolidated from original references/workflows.md (526 lines). Relocated inline: core diagnostic checking logic (Workflow Step 5), model comparison basics (Workflow Step 7), prior predictive check (Workflow Step 3), linear/logistic/hierarchical model code (Recipes). Omitted: complete monolithic workflow template (redundant with 8-step Workflow section).

Per-reference-file disposition (all originals)

Original references/ (3 files):

1. distributions.md (320 lines) -> consolidated with sampling_inference.md into new references/distributions_inference.md
2. sampling_inference.md (424 lines) -> consolidated with distributions.md into new references/distributions_inference.md
3. workflows.md (526 lines) -> migrated as new references/advanced_workflows.md

Original scripts/ (2 files):

1. model_diagnostics.py (350 lines) -> check_diagnostics() logic (R-hat, ESS, divergences, tree depth checks) inlined in Workflow Step 5; create_diagnostic_report() plot generation patterns described in Expected Outputs and referenced in references/advanced_workflows.md; compare_prior_posterior() utility covered in references/advanced_workflows.md
2. model_comparison.py (387 lines) -> compare_models() and check_loo_reliability() patterns inlined in Workflow Step 7 (using az.compare and az.loo directly); model_averaging() pattern covered in references/advanced_workflows.md; cross_validation_comparison() guidance covered in references/advanced_workflows.md; plot_model_comparison() is a thin wrapper around az.plot_compare (inlined in Step 7)

Related Skills

• arviz (planned) -- posterior visualization and diagnostics; PyMC returns ArviZ InferenceData objects
• bambi (planned) -- formula-based interface to PyMC (R-style y ~ x1 + x2 syntax); simpler API for standard GLMs
• numpyro (planned) -- JAX-based probabilistic programming; faster NUTS via hardware acceleration
• statsmodels-statistical-modeling -- frequentist regression and GLMs; use when Bayesian framework is not needed
• scikit-learn-machine-learning -- ML classification/regression; use when prediction accuracy matters more than uncertainty quantification

References

• PyMC documentation -- official API reference and tutorials
• PyMC GitHub -- source code and issue tracker
• ArviZ documentation -- posterior analysis and visualization
• Bayesian Modeling and Computation in Python -- textbook using PyMC
• PyMC examples gallery -- curated notebook collection