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This pull request introduces an initial implementation steps for adding Bayesian MCMC (DREAM) sampling and analysis to EasyReflectometryLib, with a focus on a clean user-facing API and modular architecture.
The most important changes are:
Design and Implementation Plan
BAYESIAN_IN_ERL.md, a detailed implementation plan outlining the separation of classical optimization and Bayesian sampling APIs, the introduction of asample()method for MCMC, new analysis modules, dependency requirements, and the testing strategy.Analysis Module Creation
src/easyreflectometry/analysis/__init__.pyto expose Bayesian analysis utilities, includingPosteriorResults, plotting, summary, credible interval, and posterior predictive functions, making them available for import throughout the library.Public API Enhancement
src/easyreflectometry/__init__.pyto exportPosteriorResultsin the library’s public API, ensuring that users can directly access Bayesian analysis results and utilities.