PyMC3 relies on Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution of a model's parameters. It uses a high-level syntax that integrates seamlessly with other scientific Python libraries such as NumPy and Pandas. The primary steps involve defining a probabilistic model, specifying prior distributions, and using observed data to perform inference.
