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Metropolis-Adjusted Langevin Algorithm (MALA)

Source: R/mala.R
mala.Rd

Sample from a posterior using MALA: a Langevin proposal driven by the gradient of the log posterior, corrected by a Metropolis- Hastings accept/reject step. Each iteration is one log-posterior + one gradient evaluation. The natural stepping stone between random-walk MH and HMC: cheaper per iteration than HMC (no leapfrog integration), better-mixing than RWMH because the drift term moves toward higher density.

Step size epsilon is adapted via dual averaging during warmup to target an acceptance rate of 0.574 (Roberts & Rosenthal 1998 optimal for high-dimensional Gaussians).

Usage

mala(
  log_posterior,
  grad_log_posterior,
  init,
  n_iter = 2000L,
  warmup = n_iter%/%2L,
  epsilon = 0.1,
  target_accept = 0.574,
  mass_diag = NULL,
  thin = 1L,
  verbose = FALSE
)

Arguments

log_posterior

Function function(theta) -> numeric returning the unnormalized log posterior.

grad_log_posterior

Function function(theta) -> numeric vector returning the gradient at theta.

init

Numeric vector: initial state (must give finite log_posterior).

n_iter

Total iterations including warmup (default 2000).

warmup

Warmup iterations (step-size adaptation + discarded; default n_iter / 2).

epsilon

Initial step size (default 0.1). Adapted during warmup.

target_accept

Target acceptance during warmup adaptation (default 0.574, the Roberts & Rosenthal 1998 optimum).

mass_diag

Optional preconditioner: a length-d vector of per-dimension variances, used as the diagonal inverse-mass M^-1. The proposal is N(theta + (eps^2/2) * M^-1 * grad, eps^2 * M^-1), so entry j scales the proposal variance along dimension j and the noise standard deviation is eps * sqrt(mass_diag[j]). Default rep(1, d). For posteriors with very different scales across dimensions, set this to (an estimate of) the posterior variances, i.e. the squared posterior SDs.

thin

Keep every thin-th post-warmup sample (default 1).

verbose

Print acceptance + step-size summary at end (default FALSE).

Value

A list with class tulpa_fit carrying:

  • draws: post-warmup draws.

  • means, n_params, n_samples, log_prob.

  • accept_prob: per-iteration accepted indicators (post-warmup).

  • mean_accept: post-warmup acceptance rate.

  • epsilon: final adapted step size.

  • inference_mode: "exact".

  • inference_tier: 1L.

  • backend: "mala".

Tier

Tier 1 (Exact). The MH step makes the chain asymptotically correct. Mixing depends on whether the gradient gives useful local geometry – poor for posteriors with very different scales across dimensions (use a preconditioner or HMC instead).

References

Roberts, G. O., & Tweedie, R. L. (1996). Exponential convergence of Langevin distributions and their discrete approximations. Bernoulli, 2(4), 341-363.

Roberts, G. O., & Rosenthal, J. S. (1998). Optimal scaling of discrete approximations to Langevin diffusions. JRSS B, 60(1), 255-268.

Examples

log_post <- function(t) -0.5 * sum((t - c(1, 2))^2)
grad <- function(t) -(t - c(1, 2))
fit <- mala(log_post, grad, init = c(0, 0), n_iter = 1000)
colMeans(fit$draws)  # near c(1, 2)
fit$mean_accept      # should adapt toward 0.574