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Awesome GitHub RepositoriesLikelihood Models
Statistical models representing the probability of observed data given a set of parameters.
Distinct from Model Construction: Focuses on the Bayesian likelihood for observed data, distinct from general machine learning model construction.
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Awesome Likelihood Models GitHub Repositories
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- pymc-devs/pymc
pymc-devs/pymc
9,650在 GitHub 上查看PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o
Constructs likelihood models that represent observed data for use in Bayesian inference.
Pythonbayesian-inferencemcmcprobabilistic-programming
