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chore(release): v0.1.4 - #399
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Bumps the version to 0.1.4 to ship #398 (spec #393), the two root fixes behind pymc-labs/pymc-marketing#3293: - `VAR.exog_prior_scale` accepts a tuple with one entry per endogenous equation (#394). - For one latent endogenous series with `lags=1` the stationarity constraint is a truncated own lag instead of a -inf Potential (#395): no boundary divergences, the JAX backend differentiates the model for that case, and the host model is clonable for any own-lag prior mean. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
thomaspinder
enabled auto-merge
November 4, 2026 09:03
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Summary
Bumps the version to 0.1.4 to ship #398 (spec #393), the two root fixes behind pymc-labs/pymc-marketing#3293: a per-equation
exog_prior_scale(#394) and a truncated own lag in place of the stationarity Potential for one latent series withlags=1(#395). Merge, then publish thev0.1.4release.Changes
pyproject.tomlanduv.lock: 0.1.3 → 0.1.4.Release notes
Draft to prepend to the generated notes:
Features
VAR.exog_prior_scaleaccepts a tuple with one entry per endogenous equation, inendog_namesorder, mirroringMinnesotaPrior.own_lag_mean; every entry must be positive and finite (Per-equation exog_prior_scale (spec #393, ticket 1) #394).Fixes
lags=1,build_in_modelenforces stationarity with a truncated own lag (latent_own_lag, truncated to (−1, 1)) instead of a −inf Potential, withBassembled as a Deterministic under its existing name and dims. No boundary divergences, prior-predictive latent paths are stationary, the JAX backend differentiates the model for that case, and the host model is clonable for any own-lag prior mean, the default of 1 included (Truncated own lag for one latent series with lags=1 (spec #393, ticket 2) #395, ADR-0017). Other latent cases keep the Potential.🤖 Generated with Claude Code