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Release 1.2.0 - #198
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Release 1.2.0#198
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1.1.0 published the fitting layer. This publishes a fit written as a file, and Bayesian inference over the same fit. #196 A fit specification (TOML) and the hjcfit command: template, check, fit, show. #197 HJCFIT.likelihood.mcmc: the posterior of a mechanism's rate constants by MCMC, following Epstein et al. (2016). LogPosterior, priors, the pilot and adaptive samplers, Chain, ESS and split-R-hat, and the Gaussian approximation from a numerical Hessian. Also HJCFitter(solver=...), the [likelihood] and [mcmc] tables, and hjcfit sample. 288 tests passing. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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1.1.0 published the fitting layer. This release publishes a fit written as a file, and Bayesian inference over the same fit.
hjcfitcommand:template,check,fit,show. Records through dcio, mechanisms through scalcs, and the search throughHJCFitter. The result is written as JSON with its provenance.HJCFIT.likelihood.mcmc: the posterior of a mechanism's rate constants by Markov chain Monte Carlo, following Epstein et al. (2016). It addsLogPosterior, uniform and log-uniform priors, the paper's pilot (Metropolis-within-Gibbs) and adaptive samplers,Chain, ESS and split-R̂, and the Gaussian approximation from a numerical Hessian. There is alsoHJCFitter(solver=...), the[likelihood]and[mcmc]tables, andhjcfit sample, which runs parallel chains.The only change in this PR is the version in
pyproject.toml. Once it is merged, the tagv1.2.0goes on the merge commit. The wheels workflow's gate checks that the tag andpyproject.tomlagree, then publishes to PyPI.288 tests passing on
master, and #197's CI was green on all three platforms.🤖 Generated with Claude Code