prior-elicitation

name: prior-elicitation description: > Load when the user is choosing priors, running prior predictive checks, calling find_constrained_prior, using PreliZ, or otherwise eliciting domain knowledge into a Bayesian model. Covers weakly informative priors, constrained priors, sensitivity analysis, and elicitation workflows. Triggers include: prior selection, elicitation, find_constrained_prior, PreliZ, prior predictive, expert/informative priors, weakly informative priors, constrained priors.

Prior Elicitation for PyMC Models

Decision Flowchart: Choosing a Prior Strategy

Do you have domain expertise or expert access?
├── YES: Can the expert quantify beliefs precisely?
│   ├── YES → Expert elicitation (SHELF protocol, PreliZ roulette/quartile)
│   └── NO → Constrained priors (find_constrained_prior, PreliZ maxent)
└── NO: Do you know the plausible scale of the parameter?
    ├── YES → Weakly informative priors (Normal, HalfNormal, Student-t)
    └── NO → Use prior predictive checks to calibrate
        └── Generate predictions → Do they cover plausible outcomes?
            ├── YES → Prior is acceptable
            └── NO → Tighten or widen prior, repeat

When to Use Each Strategy

pm.find_constrained_prior

Finds distribution parameters such that a specified mass falls within given bounds.

import pymc as pm

# "I'm 95% sure the effect is between -2 and 2"
params = pm.find_constrained_prior(
    pm.Normal,
    lower=-2, upper=2,
    mass=0.95,
    init_guess={"mu": 0, "sigma": 1},
)
# Returns: {"mu": 0.0, "sigma": 1.02}

# Use directly in model
with pm.Model() as model:
    beta = pm.Normal("beta", **params)

Common Patterns

# Positive parameter, 90% between 0.1 and 10
params = pm.find_constrained_prior(
    pm.LogNormal,
    lower=0.1, upper=10,
    mass=0.90,
    init_guess={"mu": 0, "sigma": 1},
)

# Rate parameter, 80% between 0.01 and 0.5
params = pm.find_constrained_prior(
    pm.Gamma,
    lower=0.01, upper=0.5,
    mass=0.80,
    init_guess={"alpha": 2, "mu": 0.1},
)

# Bounded parameter (probability), 95% between 0.2 and 0.8
params = pm.find_constrained_prior(
    pm.Beta,
    lower=0.2, upper=0.8,
    mass=0.95,
    init_guess={"alpha": 5, "beta": 5},
)

See references/find_constrained_prior.md for full API details.

PreliZ Integration

PreliZ is a library for prior elicitation. It provides tools to translate domain knowledge into probability distributions.

Maximum Entropy (maxent)

Find the least-informative distribution consistent with constraints:

import preliz as pz

# Least-informative Normal with 94% mass in [-1, 1]
dist = pz.maxent(pz.Normal(), -1, 1, 0.94)

# Least-informative HalfNormal with 94% mass below 5
dist = pz.maxent(pz.HalfNormal(), 0, 5, 0.94)

# Use result in PyMC
with pm.Model():
    sigma = pm.HalfNormal("sigma", sigma=dist.sigma)

Roulette (chip-and-bin elicitation)

Interactive method where an expert allocates "chips" to bins:

# Define bins and expert-allocated chip counts
pz.roulette(x_min=0, x_max=100, nrows=10)
# Opens interactive widget — expert distributes chips across bins
# Returns fitted distribution

Predictive Finder (predictive_elicitation)

Elicit priors by reasoning about observable predictions:

def my_model(x, beta0, beta1, sigma):
    mu = beta0 + beta1 * x
    return pz.Normal(mu=mu, sigma=sigma)

# Expert specifies: "when x=5, y is typically between 10 and 30"
pz.predictive_finder(my_model, target=pz.Normal())

Quartile Method

Specify distribution via expert-provided quartiles:

# Expert says: "median is 50, Q1 is 30, Q3 is 70"
dist = pz.quartile(pz.Normal(), q1=30, q2=50, q3=70)

See references/preliz.md for comprehensive PreliZ reference.

Prior Predictive Interpretation Checklist

Always run prior predictive checks before sampling:

with model:
    prior_pred = pm.sample_prior_predictive(draws=500, random_seed=42)

# prior_pred is a DataTree (ArviZ 1.0)
prior_samples = prior_pred["prior"].ds
prior_predictive = prior_pred["prior_predictive"].ds

What to Check

1. Range of predictions: Do simulated outcomes cover the plausible data range?
   • Plot: az.plot_ppc_dist(prior_pred, group="prior_predictive", kind="ecdf")
2. Impossible values: Are any simulated outcomes physically impossible?
   • Negative counts, probabilities outside [0,1], negative durations
3. Scale: Is the prior predictive spread reasonable relative to the data?
   • Too wide = vague, slow convergence. Too narrow = overly informative
4. Shape: Do simulated datasets look qualitatively like real data?
   • Bimodal when data is unimodal? Heavy tails when data is bounded?

Red Flags

• Prior predictive covers 10+ orders of magnitude — priors too vague

• 10% of samples produce impossible values — wrong distribution family or scale

• Prior predictive is concentrated on a narrow range far from data — prior is miscalibrated
• All prior predictive samples look identical — prior is too tight (dogmatic)

Expert Elicitation Workflow

SHELF-like Protocol

1. Define the parameter: What does it represent? What are its units?
2. Establish plausible range: "What is the lowest/highest value you would expect?"
3. Elicit quantiles: "What value are you 25%/50%/75% sure the parameter is below?"
4. Elicit tail behavior: "Is there any chance of extreme values? How extreme?"
5. Fit distribution: Use preliz.quartile() or pm.find_constrained_prior()
6. Validate: Show the fitted distribution back to the expert for confirmation
7. Prior predictive check: Generate predictions and ask "do these look realistic?" For prior predictive checking workflows in PyMC, see the pymc-modeling skill.

Translating Domain Knowledge

See references/elicitation_workflows.md for detailed protocols.

Sensitivity Analysis

Checking Prior Sensitivity

Conclusions are robust if they hold under different reasonable priors.

# Fit model with original priors
with pm.Model() as model_original:
    beta = pm.Normal("beta", 0, 1)
    ...
    idata_orig = pm.sample()

# Fit with wider priors
with pm.Model() as model_wide:
    beta = pm.Normal("beta", 0, 10)
    ...
    idata_wide = pm.sample()

# Fit with different family
with pm.Model() as model_robust:
    beta = pm.StudentT("beta", nu=3, mu=0, sigma=1)
    ...
    idata_robust = pm.sample()

# Compare posteriors visually
import arviz as az
az.plot_forest(
    [idata_orig, idata_wide, idata_robust],
    model_names=["Original", "Wide", "Robust"],
    var_names=["beta"],
)

What to Report

• If posteriors are similar across priors: conclusions are data-driven (robust)
• If posteriors differ substantially: conclusions are prior-sensitive — report sensitivity
• Always compare the posterior-to-prior contraction: strong contraction = data is informative

Power-scaling Sensitivity

For systematic sensitivity analysis, scale the prior log-density by a factor alpha:

# alpha > 1: stronger prior influence
# alpha < 1: weaker prior influence
# Compare posteriors across alpha in [0.5, 0.75, 1.0, 1.25, 1.5]

When posteriors are stable across alpha values, the inference is robust to prior choice.