jaxns

User Guide

  • Installation
  • Change Log

API Reference

  • jaxns
    • experimental
    • framework
    • internals
      • constraint_bijections
      • cumulative_ops
      • interp_utils
      • linalg
      • log_semiring
      • logging
      • maps
      • mixed_precision
      • namedtuple_utils
      • prefix_sum
      • pytree_utils
      • random
      • shapes
      • shrinkage_statistics
      • stats
      • tree_structure
      • types
    • nested_samplers
    • samplers
    • plotting
    • public
    • utils
    • warnings
    • PriorModelGen
    • PriorModelType
    • jaxify_likelihood()
    • Model
    • Prior
    • InvalidPriorName
    • Bernoulli
    • Beta
    • Categorical
    • ForcedIdentifiability
    • Poisson
    • UnnormalisedDirichlet
    • Empirical
    • TruncationWrapper
    • ExplicitDensityPrior
    • plot_diagnostics()
    • plot_cornerplot()
    • NestedSampler
    • resample()
    • marginalise_static_from_U()
    • marginalise_dynamic_from_U()
    • marginalise_static()
    • marginalise_dynamic()
    • maximum_a_posteriori_point()
    • evaluate_map_estimate()
    • summary()
    • analytic_posterior_samples()
    • sample_evidence()
    • bruteforce_posterior_samples()
    • bruteforce_evidence()
    • save_pytree()
    • save_results()
    • load_pytree()
    • load_results()
    • ShardedStaticNestedSampler
    • TerminationCondition
    • NestedSamplerResults
    • NestedSamplerState

Examples

  • Inference of Jones scalars observables (noisy angular quantities)
  • Lennard-Jones Potentials for modelling phase transitions in materials
  • Constant Likelihood
  • Dual Moons likelihood
  • Efficient parameter estimation
  • First the normal nested sampler (parameter_estimation=False)
  • Now with parameter estimation enabled
  • Egg-box Likelihood with Uniform Prior
  • Poisson likelihood and Gamma prior
  • Gaussian processes with outliers
  • Thin Gaussian Shells with Uniform Prior
  • Using JAXNS to globally optimise Neural Networks
  • Gradient Guided
  • Multivariate Normal Likelihood with Multivariate Normal Prior
  • OU process
  • Self-Exciting process (Hawkes process)
jaxns
  • jaxns
  • internals
  • View page source

internals

jaxns.internals

Submodules

  • constraint_bijections
  • cumulative_ops
  • interp_utils
  • linalg
  • log_semiring
  • logging
  • maps
  • mixed_precision
  • namedtuple_utils
  • prefix_sum
  • pytree_utils
  • random
  • shapes
  • shrinkage_statistics
  • stats
  • tree_structure
  • types
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