Bayesian inference with probabilistic programming.
Hello World in Turing — Linear Gaussian Model
Turing’s modelling syntax allows you to specify a model quickly and easily. Straightforward models can be expressed in the same way as complex, hierarchical models with stochastic control flow.
Advanced Markov Chain Monte Carlo Samplers
Turing provides Hamiltonian Monte Carlo sampling for differentiable posterior distributions, Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow, and Gibbs sampling which combines particle MCMC, HMC and many other MCMC algorithms.
Join the Turing community to contribute, learn, and get your questions answered.
Explore a rich ecosystem of libraries, tools, and more to support development.