Welcome to JAXNS’s documentation!
JAXNS’s Mission Statement
Our mission is to make nested sampling faster, easier, and more powerful.
- Bayesian computations with Neural Networks
- Inference of Jones scalars observables (noisy angular quantities)
- Constant Likelihood
- Dual Moons likelihood
- Egg-box Likelihood with Uniform Prior
- Poisson likelihood and Gamma prior
- Gaussian processes with outliers
- Thin Gaussian Shells with Uniform Prior
- Multivariate Normal Likelihood with Multivariate Normal Prior
- Logic rules
- Self-Exciting process (Hawkes process)