jaxns
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jaxns
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Bayesian computations with Neural Networks
Inference of Jones scalars observables (noisy angular quantities)
Lennard-Jones Potentials for modelling phase transitions in materials
Constant Likelihood
Dual Moons likelihood
Egg-box Likelihood with Uniform Prior
Evidence Maximisation
Generate data
Define the model with parameters
Poisson likelihood and Gamma prior
Gaussian processes with outliers
Gaussian processes with outliers
Thin Gaussian Shells with Uniform Prior
Introduction to JAXNS
Multivariate Normal Likelihood with Multivariate Normal Prior
Logic rules
Self-Exciting process (Hawkes process)
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