Prior Selection Guide

Table of Contents

• The Foundational Role of Priors
• Hierarchy of Prior Informativeness
• Weakly Informative Defaults
• Regression and GLM Priors
• High-Dimensional and Sparse Priors
• Hierarchical Model Priors
• Prior Predictive Checking
• Sensitivity Analysis
• Expert Elicitation
• Practical Implementation Guidelines
• Domain-Specific Guidance

The Foundational Role of Priors

In Bayesian inference, the prior distribution encodes pre-experimental knowledge, structural assumptions, and epistemic uncertainty. It acts as a weight function penalizing parameter regions inconsistent with theoretical constraints.

Key insight: While the prior’s influence typically diminishes as data increases, it remains critical in: - High-dimensional settings (p > n) - Small-sample scenarios - Models with weak identification

Analogy: Choosing a prior is like selecting foundations for a building on unknown soil. A flat prior is building without foundations—risky unless ground is solid. A weakly informative prior provides sturdy pilings that prevent collapse while adjusting as the structure settles. An informative prior builds on existing foundations—faster if correct, catastrophic if flawed.

Hierarchy of Prior Informativeness

Prior Level	Intended Use	Recommendation
Flat	“Objective” estimation; no preference	Discouraged. Can lead to improper posteriors and lacks regularization.
Super-vague	Minimal influence (e.g., N(0, 10⁶))	Not recommended. Causes numerical instability and funnel geometries.
Weakly Informative	Regularization; ruling out absurdities	Preferred default. Provides stability while letting data dominate.
Specifically Informative	Expert belief or historical data	Use with caution. Requires formal elicitation or meta-analysis.

# Examples of the hierarchy
# AVOID: Flat/super-vague
beta_bad = pm.Flat("beta")                    # improper, avoid
beta_vague = pm.Normal("beta", mu=0, sigma=1e6)  # numerical issues

# PREFERRED: Weakly informative
beta = pm.Normal("beta", mu=0, sigma=2.5)     # regularizing, stable

# USE CAREFULLY: Informative (requires justification)
beta_informed = pm.Normal("beta", mu=0.3, sigma=0.1)  # from prior study

Weakly Informative Defaults

Location Parameters (means, intercepts, coefficients)

# Standardize predictors first, then use unit-scale priors
beta = pm.Normal("beta", mu=0, sigma=1)       # coefficients on standardized scale
intercept = pm.Normal("intercept", mu=0, sigma=2.5)  # wider for intercepts

# Student-t for robustness to outlier effects (nu between 3-7)
beta = pm.StudentT("beta", nu=4, mu=0, sigma=2.5)

Scale Parameters (standard deviations)

Modern practice favors half-Normal, half-t, or Exponential priors.

# Half-Normal (recommended default)
sigma = pm.HalfNormal("sigma", sigma=1)

# Half-Student-t (robust, good for hierarchical variance components)
sigma = pm.HalfStudentT("sigma", nu=4, sigma=1)

# Exponential (stronger regularization toward 0)
sigma = pm.Exponential("sigma", lam=1)

Avoid InverseGamma for variances: Despite historical popularity, InverseGamma can be surprisingly informative near zero, pushing posteriors away from zero even when groups are identical.

# AVOID in hierarchical models:
variance = pm.InverseGamma("variance", alpha=0.01, beta=0.01)  # problematic

# PREFER:
sigma = pm.HalfNormal("sigma", sigma=1)

Correlation Matrices (LKJ Prior)

The LKJ prior is standard for correlation matrices: - η = 1: Uniform over all valid correlation matrices - η > 1: Concentrates toward identity matrix (lower correlations expected) - η < 1: Favors extreme correlations

# LKJ prior on correlation matrix
chol, corr, stds = pm.LKJCholeskyCov(
    "chol", n=n_dims, eta=2.0,  # mild shrinkage toward identity
    sd_dist=pm.HalfNormal.dist(sigma=1)
)

# Use in Multivariate Normal
vals = pm.MvNormal("vals", mu=0, chol=chol, observed=data)

Probability Parameters

# Beta prior for bounded [0, 1]
p = pm.Beta("p", alpha=1, beta=1)           # uniform
p = pm.Beta("p", alpha=2, beta=2)           # slight mode at 0.5

# Logit-normal for softer constraints
logit_p = pm.Normal("logit_p", mu=0, sigma=1.5)
p = pm.Deterministic("p", pm.math.sigmoid(logit_p))

Count/Rate Parameters

# Gamma for rates (positive, right-skewed)
rate = pm.Gamma("rate", alpha=2, beta=1)

# Log-normal when multiplicative effects expected
rate = pm.LogNormal("rate", mu=0, sigma=1)

Regression and GLM Priors

Coefficients

For standardized predictors, default to N(0, 1) or N(0, 2.5):

# Linear regression (standardized predictors)
beta = pm.Normal("beta", mu=0, sigma=1, dims="predictors")

# Logistic regression (wider to allow strong effects)
beta = pm.Normal("beta", mu=0, sigma=2.5, dims="predictors")

# Robust to outlier predictors (Student-t, 3 < nu < 7)
beta = pm.StudentT("beta", nu=4, mu=0, sigma=2.5, dims="predictors")

Intercepts

# If outcome standardized
intercept = pm.Normal("intercept", mu=0, sigma=1)

# If outcome on original scale, center on domain-reasonable value
# Example: human height in cm
intercept = pm.Normal("intercept", mu=170, sigma=20)

High-Dimensional and Sparse Priors

When p > n, regularization is essential for the bias-variance trade-off.

Laplace (Lasso) Prior

Continuous shrinkage with tall mode at zero and thick tails. Efficient but shrinks even large coefficients.

# Manual Laplace prior
beta = pm.Laplace("beta", mu=0, b=1, dims="features")

Horseshoe Prior

Global-local shrinkage: global hyperparameter shrinks all coefficients while local half-Cauchy allows large signals to escape.

# Manual horseshoe (no built-in pmx.Horseshoe exists)
tau = pm.HalfCauchy("tau", beta=1)  # global shrinkage
lam = pm.HalfCauchy("lam", beta=1, dims="features")  # local shrinkage
beta = pm.Normal("beta", mu=0, sigma=tau * lam, dims="features")

Sampling challenges: The Horseshoe creates a “double-funnel” geometry (massive spike at zero + heavy tails) that is extremely difficult for NUTS. Divergences are common unless using very high target_accept (0.99+). Consider:

1. Using the Regularized Horseshoe parameterization (see below)
2. Using simpler Laplace prior if full sparsity isn’t required
3. Increasing target_accept to 0.99 and allowing longer sampling

Regularized (Finnish) Horseshoe

Adds a finite “slab” width to prevent infinite estimates in cases of data separation (e.g., logistic regression). Must be implemented manually:

import pytensor.tensor as pt

# Regularized horseshoe (manual implementation)
# D0 = expected number of non-zero coefficients, D = total features, N = observations
tau = pm.HalfStudentT("tau", nu=2, sigma=D0 / (D - D0) * sigma / np.sqrt(N))
lam = pm.HalfStudentT("lam", nu=5, dims="features")
c2 = pm.InverseGamma("c2", alpha=1, beta=1)  # slab variance
z = pm.Normal("z", 0, 1, dims="features")

# Regularized shrinkage factor
lam_tilde = pt.sqrt(c2 / (c2 + tau**2 * lam**2))
beta = pm.Deterministic("beta", z * tau * lam * lam_tilde, dims="features")

R2D2 Prior

Induces a prior directly on R² (variance explained), often more interpretable. Available in pymc-extras.

import pymc_extras as pmx

# X must be centered; returns (residual_sigma, coefficients)
# output_sigma and input_sigma are required
output_sigma = y.std()
input_sigma = X.std(axis=0)

residual_sigma, beta = pmx.R2D2M2CP(
    "r2d2",
    output_sigma=output_sigma,
    input_sigma=input_sigma,
    dims="features",
    r2=0.5,        # prior mean R²
    r2_std=0.2,    # uncertainty in R²
)

Spike-and-Slab

“Gold standard” for variable selection using discrete mixture of point mass at zero and diffuse slab. Computationally demanding.

# Spike-and-slab (requires careful implementation)
import pytensor.tensor as pt

inclusion = pm.Bernoulli("inclusion", p=0.5, dims="features")
beta_slab = pm.Normal("beta_slab", mu=0, sigma=2, dims="features")
beta = pm.Deterministic("beta", inclusion * beta_slab, dims="features")

Hierarchical Model Priors

Hierarchical models use “partial pooling” where group estimates are informed by both their own data and the population distribution.

Variance Components

# Half-Student-t with nu=3-4 is robust default
# Prevents variances from collapsing to zero while remaining broad
sigma_group = pm.HalfStudentT("sigma_group", nu=4, sigma=1)

# For moderate pooling
sigma_group = pm.HalfNormal("sigma_group", sigma=0.5)

# AVOID: uniform on large range
sigma_group = pm.Uniform("sigma_group", 0, 100)  # problematic

Small Number of Groups

With few groups (< 5-10), broader Cauchy priors can cause underpooling. Use tighter priors:

# Few groups: more informative prior prevents underpooling
sigma_group = pm.HalfNormal("sigma_group", sigma=0.5)

# Many groups: can use broader priors
sigma_group = pm.HalfCauchy("sigma_group", beta=1)

Non-Centered vs Centered Parameterization

When group-level variance is small relative to data, use non-centered:

# Non-centered (preferred when data per group is sparse)
sigma_group = pm.HalfNormal("sigma_group", sigma=1)
z = pm.Normal("z", mu=0, sigma=1, dims="groups")
group_effect = pm.Deterministic("group_effect", z * sigma_group, dims="groups")

# Centered (can work when lots of data per group)
sigma_group = pm.HalfNormal("sigma_group", sigma=1)
group_effect = pm.Normal("group_effect", mu=0, sigma=sigma_group, dims="groups")

Prior Predictive Checking

Always simulate from priors before fitting to validate that priors generate plausible data.

with model:
    prior_pred = pm.sample_prior_predictive(draws=500)

# Visualize prior predictive distribution
import arviz as az
az.plot_ppc(prior_pred, group="prior", kind="cumulative")

# Numerical summary
prior_y = prior_pred.prior_predictive["y"].values.flatten()
print(f"Prior predictive range: [{prior_y.min():.2f}, {prior_y.max():.2f}]")
print(f"Prior predictive mean: {prior_y.mean():.2f}")
print(f"Prior predictive std: {prior_y.std():.2f}")

Warning Signs

Problem	Symptom	Solution
Implausible values	Negative counts, >100% probabilities, heights > 10m	Tighten priors or add constraints
Too concentrated	Prior predictive doesn’t cover observed data range	Widen priors
Extremely wide	Prior generates absurd values (humans 100m tall)	Use domain knowledge to constrain
Miscentered	Prior predictive systematically biased	Adjust location parameters

# Example: checking for implausible values
prior_y = prior_pred.prior_predictive["y"].values.flatten()

# For count data - should be non-negative
if (prior_y < 0).any():
    print("WARNING: Prior allows negative counts!")

# For proportions - should be in [0, 1]
if (prior_y < 0).any() or (prior_y > 1).any():
    print("WARNING: Prior allows invalid proportions!")

# For physical measurements - check against domain knowledge
if prior_y.max() > 300:  # e.g., human height in cm
    print("WARNING: Prior allows implausible heights!")

Sensitivity Analysis

Evaluates posterior stability under prior perturbations. Critical in small datasets where prior influence is strongest.

Basic Sensitivity Check

def fit_with_prior(prior_sd):
    """Fit model with different prior scales."""
    with pm.Model() as m:
        beta = pm.Normal("beta", mu=0, sigma=prior_sd)
        sigma = pm.HalfNormal("sigma", sigma=1)
        y = pm.Normal("y", mu=beta * X, sigma=sigma, observed=y_obs)
        trace = pm.sample()
    return trace

# Compare posteriors under different priors
traces = {}
for sd in [0.5, 1.0, 2.0, 5.0]:
    traces[sd] = fit_with_prior(sd)

# Check if posteriors are similar
for sd, trace in traces.items():
    print(f"Prior SD={sd}: posterior mean={trace.posterior['beta'].mean():.3f}")

Prior-Likelihood Conflict

Occurs when data substantially contradicts the prior. Can detect using prior-posterior comparison:

# Compare prior and posterior
az.plot_dist_comparison(trace, var_names=["beta"])

# Large divergence suggests prior-likelihood conflict
prior_mean = 0  # from prior specification
posterior_mean = trace.posterior["beta"].mean().item()
posterior_sd = trace.posterior["beta"].std().item()

# Check if prior is many SDs from posterior
if abs(prior_mean - posterior_mean) > 3 * posterior_sd:
    print("WARNING: Potential prior-likelihood conflict")

Expert Elicitation

When data is scarce (rare diseases, novel phenomena), priors must be constructed through formal elicitation. For formal elicitation protocols (SHELF framework, roulette method, expert aggregation), consult domain-specific elicitation literature such as O’Hagan et al. (2006) Uncertain Judgements: Eliciting Experts’ Probabilities.

Practical Implementation Guidelines

1. Scale Your Data

Rescale predictors and outcomes to unit scale (order of magnitude ~1):

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

# Standardize outcome (optional but helpful)
y_scaled = (y - y.mean()) / y.std()

# Now use simple priors
with pm.Model() as model:
    beta = pm.Normal("beta", mu=0, sigma=1, dims="predictors")
    intercept = pm.Normal("intercept", mu=0, sigma=1)
    sigma = pm.HalfNormal("sigma", sigma=1)

    mu = intercept + pm.math.dot(X_scaled, beta)
    y_obs = pm.Normal("y", mu=mu, sigma=sigma, observed=y_scaled)

2. Use Coords and Dims

Makes prior specification clearer and InferenceData more interpretable:

coords = {
    "predictors": ["age", "income", "education"],
    "obs_id": range(len(y))
}

with pm.Model(coords=coords) as model:
    beta = pm.Normal("beta", mu=0, sigma=1, dims="predictors")

3. Document Your Prior Choices

# Good practice: document prior justification
with pm.Model() as model:
    # Prior: N(0, 2.5) allows coefficients up to ~5 on standardized scale
    # Justification: covers effect sizes seen in similar studies
    beta = pm.Normal("beta", mu=0, sigma=2.5, dims="predictors")

    # Prior: HalfNormal(1) - weakly informative, allows broad range
    # On standardized outcome, SD > 3 would be extreme
    sigma = pm.HalfNormal("sigma", sigma=1)

4. Reparameterize When Needed

If models show poor convergence (funnels, divergences), reparameterize:

# Centered (may cause funnels with small group sizes)
with pm.Model() as model_centered:
    mu = pm.Normal("mu", mu=0, sigma=1)
    sigma_group = pm.HalfNormal("sigma_group", sigma=1)
    group_effect = pm.Normal("group_effect", mu=mu, sigma=sigma_group, dims="groups")

# Non-centered (more robust)
with pm.Model() as model_noncentered:
    mu = pm.Normal("mu", mu=0, sigma=1)
    sigma_group = pm.HalfNormal("sigma_group", sigma=1)
    z = pm.Normal("z", mu=0, sigma=1, dims="groups")  # standard normal
    group_effect = pm.Deterministic("group_effect", mu + z * sigma_group, dims="groups")

Domain-Specific Guidance

Biostatistics/Epidemiology

# Log-odds ratios: N(0, 2.5) allows ORs from ~0.01 to ~100
log_or = pm.Normal("log_or", mu=0, sigma=2.5)

# Hazard ratios: similar on log scale
log_hr = pm.Normal("log_hr", mu=0, sigma=1)

# Prevalence: Beta unless strong prior info
prevalence = pm.Beta("prevalence", alpha=1, beta=1)  # or (2, 2) for mode at 0.5

Economics/Social Science

# Elasticities on log-log models
elasticity = pm.Normal("elasticity", mu=0, sigma=1)

# Treatment effects: center on 0, SD based on plausible effect sizes
treatment_effect = pm.Normal("treatment", mu=0, sigma=0.5)

# Time trends: small per-period changes
time_trend = pm.Normal("trend", mu=0, sigma=0.1)

Physical Sciences

# Incorporate physical constraints
# Example: diffusion coefficient (must be positive)
D = pm.LogNormal("D", mu=np.log(1e-9), sigma=0.5)  # centered on typical value

# Use informative priors from previous experiments
# Example: speed of sound with known uncertainty
c = pm.Normal("c", mu=343, sigma=5)  # m/s in air at 20°C

Machine Learning / Prediction

# Focus on predictive performance
# Use shrinkage priors for automatic relevance determination
import pymc_extras as pmx

# R2D2 prior - requires output_sigma and input_sigma
output_sigma = y.std()
input_sigma = X.std(axis=0)

residual_sigma, beta = pmx.R2D2M2CP(
    "r2d2",
    output_sigma=output_sigma,
    input_sigma=input_sigma,
    dims="features",
    r2=0.5,
    r2_std=0.25,
)

# Evaluate via LOO-CV
trace = pm.sample()
loo = az.loo(trace)