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v0.8.0

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@github-actions github-actions released this 15 May 19:39
· 69 commits to main since this release
v0.8.0
015b35a

Hierarchical-model usability: fit now exposes method= / guide= / reparameterize= knobs, random-effect terms emit non-centered by default (no more funnel), and the NUTS / HMC scalar-vs-plate broadcasting bug is fixed. Tutorial 8 walks the formula → fit → diagnostics pipeline end-to-end against the running runtime.

Changed

  • BREAKING: fit(..., sampler=...) renamed to fit(..., method=...). SVI is not a sampler (it fits a variational Guide by optimization); the parameter selects an inference algorithm. The literal values ("nuts", "hmc", "svi") are unchanged. Update call sites sampler="..." → method="...".
  • BREAKING: random-effect terms emitted in non-centered form by default. (slope | g) formulae now compile to a standard-Normal plate draw z_g_<slope> : g <- Normal(0, 1) plus a deterministic let alpha_g_per_row = sigma_g * z_g_<slope>[g_idx], rather than the textbook centered alpha_g : g <- Normal(0, sigma_g). Same posterior mathematically; the funnel that wrecks centered hierarchical sampling under HMC / NUTS and under-shrinks variance components under VI is gone. Pass fit(..., reparameterize="centered") to recover the previous behaviour.

Added

  • fit(..., guide=...) exposes guide selection for SVI (previously hard-coded to AutoNormalGuide). Accepts any Guide subclass: AutoMultivariateNormalGuide, AutoLowRankMultivariateNormalGuide, AutoLaplaceApproximation, AutoIAFGuide, etc. The class is constructed internally with (program, observed_names=...).
  • fit(..., reparameterize=...) controls the parameterization of random effects. "noncentered" (default; recommended by Stan / brms) or "centered". formula_to_qvr takes the same parameter; BayesianFit.reparameterize records the choice so qvr_source echoes the actual emitted form.
  • Tutorial 8: Analysis pipelines. Step-by-step walkthrough of the formula → fit → diagnostics surface on synthetic data: Gaussian SVI fit, hierarchical SVI, NUTS Bernoulli + PSIS-LOO model comparison, ArviZ posterior-predictive checks, the lens round-trip. Every Python block verified end-to-end against the running runtime.

Fixed

  • NUTS / HMC on hierarchical formula models no longer dies with Sizes of tensors must match except in dimension 1. MonadicProgram._stack_tensors broadcasts size-1 leading dims against the maximum batch in the input list, so a scalar latent (sigma) and a per-row tensor (mu) can be concatenated ahead of an indexed observe.

Full Changelog: v0.7.1...v0.8.0

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