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Version 1.0
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Version 1.0

Contents:

  • HMC Lab // Python
  • The Hamiltonian Monte Carlo algorithm
  • Installation
  • Notebooks: Examples and tutorials
    • Tutorials
      • Getting started
      • Tuning Hamiltonian Monte Carlo
      • Separate priors per dimension
      • Creating your own inverse problem
      • Running parallel Markov chains
      • Transforming existing distributions
    • Examples
      • Linear (dense) forward operator
      • Linear (sparse) forward operator
      • Locating quakes on Grimsvötn, Iceland
      • Probabilistic Elastic FWI
  • API reference
    • Distributions
      • _AbstractDistribution
      • StandardNormal1D
      • Normal
      • Laplace
      • Uniform
      • LinearMatrix
      • CompositeDistribution
      • AdditiveDistribution
      • BayesRule
      • Himmelblau
      • SourceLocation2D
      • SourceLocation3D
    • MassMatrices
      • _AbstractMassMatrix
      • Unit
      • Diagonal
    • Samplers
      • _AbstractSampler
      • RWMH
      • HMC
    • Optimizers
      • hmclab.Optimizers.gradient_descent
    • Visualization
      • marginal
      • marginal_grid
      • visualize_2_dimensions
    • Samples
  • Module index
  • Alphabetic index
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Tutorials#

  • Getting started
  • Tuning Hamiltonian Monte Carlo
  • Separate priors per dimension
  • Creating your own inverse problem
  • Running parallel Markov chains
  • Transforming existing distributions
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Getting started
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Notebooks: Examples and tutorials
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