Package index

Priors

Define the prior distribution over simulator parameters.

priors

Priors for neural simulation-based inference

prior_uniform()

Box-uniform (independent uniform) prior

prior_normal()

Independent normal prior

prior_lognormal() prior_exponential() prior_gamma() prior_beta() prior_student_t() prior_cauchy() prior_half_normal() prior_half_cauchy()

Named prior families

prior_independent()

Combine independent priors into one joint prior

prior_truncated()

Truncate a prior to a box

prior_custom()

Build a prior from arbitrary sampling / density functions

sample_prior()

Draw samples from a prior

within_support()

Test whether parameters lie within the prior support

Simulation and training

Generate training data and fit a neural posterior estimator.

simulate_for_sbi()

Run a simulator over prior draws

npe()

Neural Posterior Estimation (NPE)

npe_sequential()

Sequential NPE with truncated-prior proposals (TSNPE)

nle()

Neural Likelihood Estimation (NLE)

nre()

Neural Ratio Estimation (NRE)

save_npe() load_npe() save_nle() load_nle() save_nre() load_nre()

Save and reload a fitted model

density_estimator

Conditional density estimators

embedding_mlp()

Embedding (summary) networks for structured observations

Neural likelihood estimation

Learn a surrogate likelihood, sample it with MCMC, and export it to Stan.

log_lik()

Evaluate a surrogate likelihood

likelihood_fn()

A surrogate likelihood as a plain R function

posterior(<nsbi_nle>)

Posterior from a neural likelihood

sample(<nsbi_mcmc_posterior>)

Sample an MCMC posterior

nsbi_mcmc

MCMC over a learned likelihood

stan_code() write_stan_model() stan_data()

Export a learned likelihood to Stan

Neural ratio estimation

Learn the likelihood-to-evidence ratio with a classifier and sample it with MCMC.

log_ratio()

Evaluate a learned likelihood-to-evidence ratio

posterior(<nsbi_nre>)

Posterior from a neural ratio

Running the simulator

The simulator contract, parallel execution, and progress reporting.

nsbi_simulator

The simulator contract

nsbi_parallel

Running the simulator in parallel

nsbi_progress

Progress reporting

Working with the posterior

Condition on data, then sample, evaluate, and summarize.

posterior()

Posterior objects

sample()

Draw samples (S3 generic)

sample(<nsbi_posterior>)

Sample from a posterior

sample_posterior()

Sample from a posterior (non-generic alias)

log_prob()

Posterior log-density

map_estimate()

Maximum a posteriori (MAP) estimate

as.data.frame(<nsbi_samples>) summary(<nsbi_samples>) summary(<nsbi_posterior>) summary(<nsbi_npe>) summary(<nsbi_nle>) summary(<nsbi_nre>)

Summaries and tidy accessors

Diagnostics

Calibration and predictive checks for a fitted posterior.

diagnostics

Posterior diagnostics

sbc()

Simulation-Based Calibration (SBC)

expected_coverage()

Expected coverage of central credible intervals

tarp()

TARP expected coverage

c2st()

Classifier two-sample test (C2ST)

posterior_predictive()

Posterior predictive draws

Plotting

pairplot()

Visualize posterior samples

plot_sbc()

Plot an SBC rank histogram

plot_coverage()

Plot nominal vs. empirical credible-interval coverage

plot_tarp()

Plot TARP expected coverage

plot_posterior_predictive()

Plot posterior predictive checks

Benchmark tasks

Reference inference problems shared by the tests and benchmarks.

task_gaussian_linear() task_two_moons() task_slcp() task_sir()

Benchmark tasks