Collider physics analyses have historically favored Frequentist statistical methodologies, with some exceptions of Bayesian inference in LHC analyses through use of the Bayesian Analysis Toolkit (BAT). We demonstrate work towards an approach for performing Bayesian inference for LHC physics analyses that builds upon the existing APIs and model building technology of the pyhf and PyMC Python libraries and leverages pyhf’s automatic differentiation and hardware acceleration through its JAX computational backend. This approach presents a path toward unified APIs in pyhf that allow for users to choose a Frequentist or Bayesian approach towards statistical inference, leveraging their respective strengths as needed, without having to transition between using multiple libraries or fall back to using pyhf with BAT through the Julia programming language PyCall package. Examples of Markov chain Monte Carlo implementations using Metropolis-Hastings and Hamiltonian Monte Carlo are presented.
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