tulpa_nuts_beta: Fit a beta-regression model via NUTS (joint sampling of beta...

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Fit a beta-regression model via NUTS (joint sampling of beta + log_phi)

Description

Bayesian counterpart to tulpa_laplace_beta(). Routes the Beta GLM through tulpa's full NUTS backend via the generic LikelihoodSpec interface, sampling the precision phi jointly with the regression coefficients on the log scale. NUTS performs exact marginalisation over phi, replacing the Brent outer-opt in tulpa_laplace_beta() and the deterministic nested-Laplace grid that would otherwise integrate over the Beta dispersion.

Mean-precision parameterisation, default logit link: y_i ~ Beta(mu_i * phi, (1 - mu_i) * phi) with mu_i = 1 / (1 + exp(-eta_i)) and eta_i = X_i %*% beta.

Usage

tulpa_nuts_beta(
  y,
  X,
  beta_prior = .tulpa_default_beta_prior("beta_nuts"),
  log_phi_prior_sd = 3,
  log_phi_init = 0,
  control = list()
)

Arguments

y	Response vector, strictly in ⁠(0, 1)⁠.
X	Fixed-effects design matrix.
beta_prior	Fixed-effect prior as list(mean, sd): a mean-zero (mean = 0) Gaussian on each coefficient with SD sd (default the engine default, prior_normal(0, 2.5)).
log_phi_prior_sd	Prior SD on log(phi) (log_phi ~ N(0, log_phi_prior_sd)). Default 3 (very weak; covers phi from ~0.001 to ~1000 within +-2 SD).
log_phi_init	Starting value for log(phi). Default 0 (i.e. phi = 1); a method-of-moments warm start can speed warmup in highly concentrated regimes.
control	A named list of numerical / sampler knobs (statistical arguments stay in the signature): n_iter (default 2000), n_warmup (default 1000), max_treedepth (default 10), adapt_delta (default 0.8), seed (NULL draws from the session RNG), verbose (default FALSE).

Value

A tulpa_fit object with:

• draws – ⁠n_samples x (p + 1)⁠ matrix of post-warmup draws, columns named from colnames(X) (falling back to ⁠beta[1] ... beta[p]⁠) then log_phi.

• means – posterior means.

• phi_summary – posterior mean / median / quantiles of phi = exp(log_phi).

• accept_prob, divergent, treedepth, epsilon – NUTS diagnostics from the underlying chain.

See Also

tulpa_laplace_beta() for the Laplace + Brent point estimate; tulpa_laplace() for the underlying Laplace engine.

Examples

set.seed(1)
n <- 150L
X <- cbind(1, rnorm(n))
mu <- plogis(X %*% c(0.2, 0.7)); phi <- 8
y <- rbeta(n, mu * phi, (1 - mu) * phi)

fit <- tulpa_nuts_beta(y, X, control = list(n_iter = 500L, n_warmup = 250L))
colMeans(fit$draws)

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.