cathedral 0.3.0

A Pythonic probabilistic programming library inspired by Church and WebPPL

Cathedral

A Pythonic probabilistic programming library inspired by Church and WebPPL. Write probabilistic models as plain Python functions, then run inference to get posteriors.

Cathedral fills a gap in the Python ecosystem: Church/WebPPL-level expressiveness (stochastic control flow, recursive models, stochastic memoization) with Pythonic syntax and access to Python's scientific computing stack.

Quick Start

from cathedral import model, infer, flip

@model
def sprinkler():
    rain = flip(0.3)
    sprinkler_on = flip(0.5)
    if rain:
        wet = flip(0.9)
    elif sprinkler_on:
        wet = flip(0.8)
    else:
        wet = flip(0.1)
    return {"rain": rain, "sprinkler": sprinkler_on, "wet": wet}

# One model, many queries — pass the condition externally (Church-style)
posterior = infer(sprinkler, method="enumerate", condition=lambda r: r["wet"])
print(f"P(rain | wet grass) = {posterior.probability('rain'):.4f}")
# P(rain | wet grass) = 0.4615

posterior = infer(sprinkler, method="enumerate",
                 condition=lambda r: r["wet"] and r["sprinkler"])
print(f"P(rain | wet, sprinkler on) = {posterior.probability('rain'):.4f}")
# P(rain | wet, sprinkler on) = 0.3253  (explaining away!)

Installation

pip install cathedral

# With visualization extras (matplotlib, graphviz, arviz)
pip install cathedral[viz]

Requires Python >= 3.10, numpy, and scipy.

Bayesian Inference in Action

Medical Diagnosis

A disease has 1% base rate, but produces two symptoms with high probability. What happens when a patient presents with both?

@model
def medical_diagnosis():
    disease = flip(0.01)
    if disease:
        symptom_a = flip(0.9)
        symptom_b = flip(0.8)
    else:
        symptom_a = flip(0.05)
        symptom_b = flip(0.1)
    condition(symptom_a and symptom_b)
    return disease

posterior = infer(medical_diagnosis, method="enumerate")

Despite the low base rate, observing both symptoms raises P(disease) to 59% — a classic base-rate neglect problem that exact enumeration gets right.

Bayesian Linear Regression

@model
def line_model():
    slope = sample(Normal(0, 5), name="slope")
    intercept = sample(Normal(0, 5), name="intercept")
    for x, y in zip(xs, ys):
        observe(Normal(slope * x + intercept, 0.5), y)
    return {"slope": slope, "intercept": intercept}

posterior = infer(line_model, method="importance", num_samples=10000)

Metropolis-Hastings: Inferring a Gaussian Mean

Five observations near 3.0. MH explores the posterior, and the samples match the analytical solution:

@model
def gaussian_mean():
    mu = sample(Normal(0, 5), name="mu")
    for y in [3.0, 3.5, 2.5, 3.2, 2.8]:
        observe(Normal(mu, 1), y)
    return mu

posterior = infer(gaussian_mean, method="mh", num_samples=2000, burn_in=500)

Model Comparison

Observed 10 heads in a row. Is the coin fair, or biased? Bayes factors quantify the evidence:

from cathedral.checks import compare_models

p_fair = infer(fair_coin, method="importance", num_samples=10000)
p_biased = infer(biased_coin, method="importance", num_samples=10000)
print(compare_models({"fair_coin": p_fair, "biased_coin": p_biased}))

Variable-Structure Traces

Unlike PyMC or Stan, Cathedral supports stochastic control flow — different executions can have entirely different random variables. The toolkit tracks which addresses appear in which traces:

@model
def animal():
    is_bird = flip(0.3, name="is_bird")
    if is_bird:
        can_fly = flip(0.8, name="can_fly")
        has_beak = flip(0.99, name="has_beak")
        return {"type": "bird", "flies": can_fly}
    else:
        is_mammal = flip(0.7, name="is_mammal")
        if is_mammal:
            has_fur = flip(0.95, name="has_fur")
            return {"type": "mammal", "fur": has_fur}
        else:
            is_cold_blooded = flip(0.8, name="is_cold_blooded")
            return {"type": "reptile"}

Primitives

Primitive	Description
flip(p)	Flip a coin with probability p of True
sample(dist)	Draw from any distribution (Normal, Beta, Gamma, ...)
condition(pred)	Hard conditioning: reject execution if pred is False
observe(dist, val)	Soft conditioning: score execution by dist.log_prob(val)
factor(score)	Add arbitrary log-probability to the trace
mem(fn)	Stochastic memoization: same args always return same random result
DPmem(alpha, fn)	Dirichlet Process memoization for nonparametric models

Inference Methods

Method	Syntax	Best for
Rejection sampling	infer(m, method="rejection")	Small discrete models with condition()
Importance sampling	infer(m, method="importance")	Continuous models with observe()
Single-site MH	infer(m, method="mh")	Complex models, rare conditions, many latent variables
Exact enumeration	infer(m, method="enumerate")	Small discrete models where you want exact answers

Reproducible Inference

Rejection, importance, and MH support seeded execution:

posterior = infer(my_model, method="importance", num_samples=5000, seed=123)

Reproducibility contract:

• Same seed should give identical results for the same inference call.
• Seeded reproducibility covers random choices made through Cathedral primitives and distributions.
• Raw np.random calls inside user model code are outside Cathedral's RNG context.
• Unseeded runs continue to use Cathedral's internal default RNG behavior.

Weighted Importance Sampling

Importance sampling defaults to resampling, which returns an unweighted posterior:

posterior = infer(my_model, method="importance", num_samples=5000)

For diagnostics or lower-variance analysis, you can keep weighted traces:

posterior = infer(
    my_model,
    method="importance",
    num_samples=5000,
    resample=False,
    seed=123,
)

posterior.mean("param")  # uses normalized importance weights
posterior.ess            # effective sample size from the weights

Distributions

Continuous: Normal, HalfNormal, Beta, Gamma, Exponential, Uniform, LogNormal, StudentT

Discrete: Bernoulli, Categorical, UniformDraw, Poisson, Geometric, Binomial

Multivariate: Dirichlet

All distributions support .sample(), .log_prob(value), and .prob(value). Discrete distributions also support .support() for enumeration.

Posterior Analysis

posterior = infer(my_model, method="rejection", num_samples=5000)

posterior.mean("param")                     # posterior mean
posterior.std("param")                      # posterior std
posterior.probability("flag")               # P(flag = True)
posterior.probability(lambda r: r > 0)      # P(predicate)
posterior.histogram("param")                # empirical distribution
posterior.credible_interval(0.95, "param")  # 95% credible interval
posterior.summary("param")                  # common numeric summaries

Diagnostics & Model Understanding

Every inference run returns diagnostic metadata automatically:

posterior = infer(sprinkler, method="rejection", num_samples=1000)
print(posterior.diagnostics())
# Posterior: 1000 samples
#   method: rejection
#   attempts: 1694
#   acceptance rate: 0.5903
#   fixed structure: True

posterior.acceptance_rate          # fraction accepted (rejection/MH)
posterior.ess                     # effective sample size (importance/enumerate)
posterior.log_marginal_likelihood  # for model comparison
posterior.has_fixed_structure      # all traces share the same addresses?

Prior Predictive Checks

from cathedral.checks import prior_predictive, condition_acceptance_rate

pp = prior_predictive(sprinkler, num_samples=5000)

# How hard is your inference problem?
rate = condition_acceptance_rate(sprinkler, num_samples=10000)
# "Condition satisfied 58.5% of the time"

Trace Visualization

from cathedral.viz import print_trace, structure_summary, trace_to_dot

posterior = infer(my_model, method="rejection", capture_scopes=True)
print_trace(posterior.traces[0])
# Trace (result=..., log_joint=-2.3026, 3 choices)
# └── [sprinkler]
#     ├── rain = False  (-1.2040)
#     ├── sprinkler = True  (-0.6931)
#     └── wet = True  (-0.2231)

print(structure_summary(posterior))

dot = trace_to_dot(posterior.traces[0])  # Graphviz DOT output

Diagnostic Plots (requires cathedral[viz])

from cathedral.plots import plot_posterior, plot_weights, plot_trace_values, plot_ess

plot_posterior(posterior, key="rain")
plot_weights(posterior)          # importance weight distribution
plot_trace_values(posterior, "mu")  # MH mixing diagnostic
plot_ess(posterior)              # ESS per address

ArviZ Integration (requires cathedral[viz])

idata = posterior.to_arviz()
# → arviz.InferenceData with posterior group, ready for az.plot_trace(), etc.

Examples

The examples/ directory contains runnable demonstrations inspired by Probabilistic Models of Cognition:

File	Topics
01_generative_models.py	Coin flips, composition, mem, stochastic recursion, causal models
02_conditioning.py	Bayesian reasoning, causal vs diagnostic inference, explaining away
03_patterns_of_inference.py	Bayesian updating, Monty Hall, Occam's razor
04_bayesian_data_analysis.py	Parameter estimation, model comparison, linear regression
05_mixture_models.py	Gaussian mixtures, DPmem for infinite components
06_social_cognition.py	Goal inference, preference learning, theory of mind
07_grammars_and_recursion.py	PCFGs, random arithmetic, conditioned generation

Plus standalone examples: sprinkler.py, coin_flip.py, linear_regression.py.

Benchmarks

A lightweight benchmark runner is included for the v0.3 inference work:

uv run python benchmarks/inference_benchmarks.py
uv run python benchmarks/inference_benchmarks.py --repeats 3 --sample-multiplier 10

The script exercises representative rejection and importance models and reports:

• wall-clock time
• attempts/sec for rejection-based cases
• ESS and ESS/sec for importance-based cases
• averages across repeated runs

The v0.3 benchmark work also evaluated thread-based parallel rejection and importance sampling. It was deferred because the tested workloads were mostly flat or slightly slower with worker threads; seeded serial inference remains the supported v0.3 path.

Current benchmark cases:

• rejection_sprinkler
• rejection_bag_of_marbles
• importance_coin_bias_estimation
• importance_bayesian_regression

Architecture

Models are plain Python functions. A trace-based execution engine (via contextvars) records every random choice without passing trace objects through user code. Inference engines run models repeatedly, using interventions to replay or modify choices.

User code           Trace engine           Inference
─────────           ────────────           ─────────
@model fn    →    TraceContext      →    rejection / importance
flip/sample  →    Choice records    →    MH (propose + accept)
condition    →    log_score         →    enumeration (worklist)
observe      →    log_score         →    Posterior + diagnostics

References

• Church: A Language for Generative Models -- Goodman, Mansinghka, Roy, Bonawitz, Tenenbaum
• Lightweight Implementations of Probabilistic Programming Languages -- Wingate, Stuhlmuller, Goodman
• Probabilistic Models of Cognition -- Goodman & Tenenbaum
• WebPPL -- Goodman & Stuhlmuller
• Gen.jl -- Cusumano-Towner, Saad, Lew, Mansinghka
• From Word Models to World Models -- Wong, Grand, Lew, Goodman et al.

License

MIT -- see LICENSE.