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  • First steps
  • User Guide
  • Examples
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User Guide

  • Problem Description
    • hopsy.Model
      • hopsy.Model.compute_expected_fisher_information
      • hopsy.Model.compute_log_likelihood_gradient
      • hopsy.Model.compute_negative_log_likelihood
      • hopsy.Model.log_curvature
      • hopsy.Model.log_density
      • hopsy.Model.log_gradient
    • hopsy.Gaussian
      • hopsy.Gaussian.compute_expected_fisher_information
      • hopsy.Gaussian.compute_log_likelihood_gradient
      • hopsy.Gaussian.compute_negative_log_likelihood
      • hopsy.Gaussian.log_curvature
      • hopsy.Gaussian.log_density
      • hopsy.Gaussian.log_gradient
    • hopsy.Mixture
      • hopsy.Mixture.compute_expected_fisher_information
      • hopsy.Mixture.compute_log_likelihood_gradient
      • hopsy.Mixture.compute_negative_log_likelihood
      • hopsy.Mixture.log_curvature
      • hopsy.Mixture.log_density
      • hopsy.Mixture.log_gradient
    • hopsy.Rosenbrock
      • hopsy.Rosenbrock.compute_expected_fisher_information
      • hopsy.Rosenbrock.compute_log_likelihood_gradient
      • hopsy.Rosenbrock.compute_negative_log_likelihood
      • hopsy.Rosenbrock.log_curvature
      • hopsy.Rosenbrock.log_density
      • hopsy.Rosenbrock.log_gradient
    • hopsy.Problem
      • hopsy.Problem.slacks
    • hopsy.add_box_constraints
    • hopsy.compute_chebyshev_center
    • hopsy.round
  • Markov Chain Monte Carlo Sampling
    • hopsy.AdaptiveMetropolisProposal
      • hopsy.AdaptiveMetropolisProposal.accept_proposal
      • hopsy.AdaptiveMetropolisProposal.deepcopy
      • hopsy.AdaptiveMetropolisProposal.propose
    • hopsy.BallWalkProposal
      • hopsy.BallWalkProposal.accept_proposal
      • hopsy.BallWalkProposal.deepcopy
      • hopsy.BallWalkProposal.propose
    • hopsy.CSmMALAProposal
      • hopsy.CSmMALAProposal.accept_proposal
      • hopsy.CSmMALAProposal.deepcopy
      • hopsy.CSmMALAProposal.propose
    • hopsy.DikinWalkProposal
      • hopsy.DikinWalkProposal.accept_proposal
      • hopsy.DikinWalkProposal.deepcopy
      • hopsy.DikinWalkProposal.propose
    • hopsy.GaussianCoordinateHitAndRunProposal
      • hopsy.GaussianCoordinateHitAndRunProposal.accept_proposal
      • hopsy.GaussianCoordinateHitAndRunProposal.deepcopy
      • hopsy.GaussianCoordinateHitAndRunProposal.propose
    • hopsy.GaussianHitAndRunProposal
      • hopsy.GaussianHitAndRunProposal.accept_proposal
      • hopsy.GaussianHitAndRunProposal.deepcopy
      • hopsy.GaussianHitAndRunProposal.propose
    • hopsy.GaussianProposal
      • hopsy.GaussianProposal.accept_proposal
      • hopsy.GaussianProposal.deepcopy
      • hopsy.GaussianProposal.propose
    • hopsy.UniformCoordinateHitAndRunProposal
      • hopsy.UniformCoordinateHitAndRunProposal.accept_proposal
      • hopsy.UniformCoordinateHitAndRunProposal.deepcopy
      • hopsy.UniformCoordinateHitAndRunProposal.propose
    • hopsy.UniformHitAndRunProposal
      • hopsy.UniformHitAndRunProposal.accept_proposal
      • hopsy.UniformHitAndRunProposal.deepcopy
      • hopsy.UniformHitAndRunProposal.propose
    • hopsy.MarkovChain
    • hopsy.sample
    • hopsy.RandomNumberGenerator
      • hopsy.RandomNumberGenerator.__call__
    • hopsy.Normal
      • hopsy.Normal.__call__
    • hopsy.Uniform
      • hopsy.Uniform.__call__
  • Tuning
    • hopsy.tune
    • hopsy.ThompsonSamplingTuning
    • hopsy.TuningTarget
    • hopsy.AcceptanceRateTarget
    • hopsy.ExpectedSquaredJumpDistanceTarget
    • hopsy.PyTuningTarget
  • Post-Processing
    • hopsy.ess
    • hopsy.mcse
    • hopsy.rhat
  • User Guide
  • Tuning

Tuning#

Reference#

Tuning#

hopsy.tune(mcs, rngs, target, n_tuning[, k, ...])

Thompson Sampling-based tuning method for specifying meaningful hyperparameters for Markov chain proposal distributions.

hopsy.ThompsonSamplingTuning

hopsy.TuningTarget

hopsy.AcceptanceRateTarget

hopsy.ExpectedSquaredJumpDistanceTarget

hopsy.PyTuningTarget

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