[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-pymc":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"The Python probabilistic programming library for modern Bayesian inference.","PyMC describes a statistical model as a graph of random variables, then fits it with MCMC sampling (NUTS, Hamiltonian Monte Carlo) or variational inference on a PyTensor backend that compiles to C, JAX or Numba. Results come out as ArviZ objects for convergence diagnostics, credible intervals and model comparison. It's our choice when we need to quantify uncertainty, encode domain priors, or work with small datasets using hierarchical models.",[7,8,9,10],"NUTS sampling and variational inference, with JAX and Numba backends.","Hierarchical models and explicit priors: robust on small datasets.","ArviZ diagnostics: convergence, credible intervals, model comparison.","Ecosystem: PyMC-Marketing (media mix, CLV), pymc-bart, Gaussian processes.","Bayesian A\u002FB testing, media mix modeling, forecasting with uncertainty, reliability and risk.",1792675979729]