Welcome to the pints documentation¶
Pints is hosted on GitHub, where you can find downloads and installation instructions.
Detailed examples can also be found there.
This page provides the API, or developer documentation for pints
.
Contents¶
- ABC samplers
- ABC sampler base class
ABCSampler
ABCController
ABCController.log_filename()
ABCController.max_iterations()
ABCController.n_samples()
ABCController.parallel()
ABCController.run()
ABCController.sampler()
ABCController.set_log_interval()
ABCController.set_log_to_file()
ABCController.set_log_to_screen()
ABCController.set_max_iterations()
ABCController.set_n_samples()
ABCController.set_parallel()
- ABC-SMC sampler
- Rejection ABC sampler
- ABC sampler base class
- Boundaries
- Core classes and methods
- Diagnosing MCMC results
- Diagnostic plots
- Error measures
- Function evaluation
- I/O Helper classes
- Log-likelihoods
AR1LogLikelihood
ARMA11LogLikelihood
CauchyLogLikelihood
CensoredGaussianLogLikelihood
ConstantAndMultiplicativeGaussianLogLikelihood
GaussianIntegratedLogUniformLogLikelihood
GaussianIntegratedUniformLogLikelihood
GaussianKnownSigmaLogLikelihood
GaussianLogLikelihood
KnownNoiseLogLikelihood
LogNormalLogLikelihood
MultiplicativeGaussianLogLikelihood
ScaledLogLikelihood
StudentTLogLikelihood
UnknownNoiseLogLikelihood
- Log-PDFs
- Log-priors
BetaLogPrior
CauchyLogPrior
ComposedLogPrior
ExponentialLogPrior
GammaLogPrior
GaussianLogPrior
HalfCauchyLogPrior
InverseGammaLogPrior
LogNormalLogPrior
LogUniformLogPrior
MultivariateGaussianLogPrior
MultivariateGaussianLogPrior.cdf()
MultivariateGaussianLogPrior.convert_from_unit_cube()
MultivariateGaussianLogPrior.convert_to_unit_cube()
MultivariateGaussianLogPrior.evaluateS1()
MultivariateGaussianLogPrior.icdf()
MultivariateGaussianLogPrior.mean()
MultivariateGaussianLogPrior.n_parameters()
MultivariateGaussianLogPrior.pseudo_cdf()
MultivariateGaussianLogPrior.pseudo_icdf()
MultivariateGaussianLogPrior.sample()
NormalLogPrior
StudentTLogPrior
TruncatedGaussianLogPrior
TruncatedGaussianLogPrior.cdf()
TruncatedGaussianLogPrior.convert_from_unit_cube()
TruncatedGaussianLogPrior.convert_to_unit_cube()
TruncatedGaussianLogPrior.evaluateS1()
TruncatedGaussianLogPrior.icdf()
TruncatedGaussianLogPrior.mean()
TruncatedGaussianLogPrior.n_parameters()
TruncatedGaussianLogPrior.sample()
UniformLogPrior
- MCMC Samplers
- Running an MCMC routine
mcmc_sample()
MCMCController
MCMCController.chains()
MCMCController.initial_phase_iterations()
MCMCController.log_pdfs()
MCMCController.max_iterations()
MCMCController.method_needs_initial_phase()
MCMCController.n_evaluations()
MCMCController.parallel()
MCMCController.run()
MCMCController.sampler()
MCMCController.samplers()
MCMCController.set_chain_filename()
MCMCController.set_chain_storage()
MCMCController.set_initial_phase_iterations()
MCMCController.set_log_interval()
MCMCController.set_log_pdf_filename()
MCMCController.set_log_pdf_storage()
MCMCController.set_log_to_file()
MCMCController.set_log_to_screen()
MCMCController.set_max_iterations()
MCMCController.set_parallel()
MCMCController.time()
MCMCSampling
MCMCSampling.chains()
MCMCSampling.initial_phase_iterations()
MCMCSampling.log_pdfs()
MCMCSampling.max_iterations()
MCMCSampling.method_needs_initial_phase()
MCMCSampling.n_evaluations()
MCMCSampling.parallel()
MCMCSampling.run()
MCMCSampling.sampler()
MCMCSampling.samplers()
MCMCSampling.set_chain_filename()
MCMCSampling.set_chain_storage()
MCMCSampling.set_initial_phase_iterations()
MCMCSampling.set_log_interval()
MCMCSampling.set_log_pdf_filename()
MCMCSampling.set_log_pdf_storage()
MCMCSampling.set_log_to_file()
MCMCSampling.set_log_to_screen()
MCMCSampling.set_max_iterations()
MCMCSampling.set_parallel()
MCMCSampling.time()
- MCMC Sampler base classes
MCMCSampler
SingleChainMCMC
SingleChainMCMC.ask()
SingleChainMCMC.in_initial_phase()
SingleChainMCMC.n_hyper_parameters()
SingleChainMCMC.name()
SingleChainMCMC.needs_initial_phase()
SingleChainMCMC.needs_sensitivities()
SingleChainMCMC.replace()
SingleChainMCMC.set_hyper_parameters()
SingleChainMCMC.set_initial_phase()
SingleChainMCMC.tell()
MultiChainMCMC
MultiChainMCMC.ask()
MultiChainMCMC.current_log_pdfs()
MultiChainMCMC.in_initial_phase()
MultiChainMCMC.n_hyper_parameters()
MultiChainMCMC.name()
MultiChainMCMC.needs_initial_phase()
MultiChainMCMC.needs_sensitivities()
MultiChainMCMC.set_hyper_parameters()
MultiChainMCMC.set_initial_phase()
MultiChainMCMC.tell()
- Adaptive Covariance MC
AdaptiveCovarianceMC
AdaptiveCovarianceMC.acceptance_rate()
AdaptiveCovarianceMC.ask()
AdaptiveCovarianceMC.eta()
AdaptiveCovarianceMC.in_initial_phase()
AdaptiveCovarianceMC.n_hyper_parameters()
AdaptiveCovarianceMC.name()
AdaptiveCovarianceMC.needs_initial_phase()
AdaptiveCovarianceMC.needs_sensitivities()
AdaptiveCovarianceMC.replace()
AdaptiveCovarianceMC.set_eta()
AdaptiveCovarianceMC.set_hyper_parameters()
AdaptiveCovarianceMC.set_initial_phase()
AdaptiveCovarianceMC.set_target_acceptance_rate()
AdaptiveCovarianceMC.target_acceptance_rate()
AdaptiveCovarianceMC.tell()
- Differential Evolution MCMC
DifferentialEvolutionMCMC
DifferentialEvolutionMCMC.ask()
DifferentialEvolutionMCMC.current_log_pdfs()
DifferentialEvolutionMCMC.gamma()
DifferentialEvolutionMCMC.gamma_switch_rate()
DifferentialEvolutionMCMC.gaussian_error()
DifferentialEvolutionMCMC.in_initial_phase()
DifferentialEvolutionMCMC.n_hyper_parameters()
DifferentialEvolutionMCMC.name()
DifferentialEvolutionMCMC.needs_initial_phase()
DifferentialEvolutionMCMC.needs_sensitivities()
DifferentialEvolutionMCMC.relative_scaling()
DifferentialEvolutionMCMC.scale_coefficient()
DifferentialEvolutionMCMC.set_gamma()
DifferentialEvolutionMCMC.set_gamma_switch_rate()
DifferentialEvolutionMCMC.set_gaussian_error()
DifferentialEvolutionMCMC.set_hyper_parameters()
DifferentialEvolutionMCMC.set_initial_phase()
DifferentialEvolutionMCMC.set_relative_scaling()
DifferentialEvolutionMCMC.set_scale_coefficient()
DifferentialEvolutionMCMC.tell()
- Dram ACMC
DramACMC
DramACMC.acceptance_rate()
DramACMC.ask()
DramACMC.eta()
DramACMC.in_initial_phase()
DramACMC.n_hyper_parameters()
DramACMC.name()
DramACMC.needs_initial_phase()
DramACMC.needs_sensitivities()
DramACMC.replace()
DramACMC.set_eta()
DramACMC.set_hyper_parameters()
DramACMC.set_initial_phase()
DramACMC.set_sigma_scale()
DramACMC.set_target_acceptance_rate()
DramACMC.sigma_scale()
DramACMC.target_acceptance_rate()
DramACMC.tell()
- DreamMCMC
DreamMCMC
DreamMCMC.CR()
DreamMCMC.ask()
DreamMCMC.b()
DreamMCMC.b_star()
DreamMCMC.constant_crossover()
DreamMCMC.current_log_pdfs()
DreamMCMC.delta_max()
DreamMCMC.in_initial_phase()
DreamMCMC.nCR()
DreamMCMC.n_hyper_parameters()
DreamMCMC.name()
DreamMCMC.needs_initial_phase()
DreamMCMC.needs_sensitivities()
DreamMCMC.p_g()
DreamMCMC.set_CR()
DreamMCMC.set_b()
DreamMCMC.set_b_star()
DreamMCMC.set_constant_crossover()
DreamMCMC.set_delta_max()
DreamMCMC.set_hyper_parameters()
DreamMCMC.set_initial_phase()
DreamMCMC.set_nCR()
DreamMCMC.set_p_g()
DreamMCMC.tell()
- Dual Averaging
DualAveragingAdaption
DualAveragingAdaption.adapt_epsilon()
DualAveragingAdaption.add_parameter_sample()
DualAveragingAdaption.calculate_sample_variance()
DualAveragingAdaption.final_epsilon()
DualAveragingAdaption.get_epsilon()
DualAveragingAdaption.get_mass_matrix()
DualAveragingAdaption.init_adapt_epsilon()
DualAveragingAdaption.init_sample_covariance()
DualAveragingAdaption.set_inv_mass_matrix()
DualAveragingAdaption.step()
DualAveragingAdaption.target_accept_prob()
DualAveragingAdaption.use_dense_mass_matrix()
DualAveragingAdaption.warmup_steps()
- EmceeHammerMCMC
EmceeHammerMCMC
EmceeHammerMCMC.ask()
EmceeHammerMCMC.current_log_pdfs()
EmceeHammerMCMC.in_initial_phase()
EmceeHammerMCMC.n_hyper_parameters()
EmceeHammerMCMC.name()
EmceeHammerMCMC.needs_initial_phase()
EmceeHammerMCMC.needs_sensitivities()
EmceeHammerMCMC.scale()
EmceeHammerMCMC.set_hyper_parameters()
EmceeHammerMCMC.set_initial_phase()
EmceeHammerMCMC.set_scale()
EmceeHammerMCMC.tell()
- Haario ACMC
HaarioACMC
HaarioACMC.acceptance_rate()
HaarioACMC.ask()
HaarioACMC.eta()
HaarioACMC.in_initial_phase()
HaarioACMC.n_hyper_parameters()
HaarioACMC.name()
HaarioACMC.needs_initial_phase()
HaarioACMC.needs_sensitivities()
HaarioACMC.replace()
HaarioACMC.set_eta()
HaarioACMC.set_hyper_parameters()
HaarioACMC.set_initial_phase()
HaarioACMC.set_target_acceptance_rate()
HaarioACMC.target_acceptance_rate()
HaarioACMC.tell()
- Haario Bardenet ACMC
HaarioBardenetACMC
HaarioBardenetACMC.acceptance_rate()
HaarioBardenetACMC.ask()
HaarioBardenetACMC.eta()
HaarioBardenetACMC.in_initial_phase()
HaarioBardenetACMC.n_hyper_parameters()
HaarioBardenetACMC.name()
HaarioBardenetACMC.needs_initial_phase()
HaarioBardenetACMC.needs_sensitivities()
HaarioBardenetACMC.replace()
HaarioBardenetACMC.set_eta()
HaarioBardenetACMC.set_hyper_parameters()
HaarioBardenetACMC.set_initial_phase()
HaarioBardenetACMC.set_target_acceptance_rate()
HaarioBardenetACMC.target_acceptance_rate()
HaarioBardenetACMC.tell()
AdaptiveCovarianceMCMC
AdaptiveCovarianceMCMC.acceptance_rate()
AdaptiveCovarianceMCMC.ask()
AdaptiveCovarianceMCMC.eta()
AdaptiveCovarianceMCMC.in_initial_phase()
AdaptiveCovarianceMCMC.n_hyper_parameters()
AdaptiveCovarianceMCMC.name()
AdaptiveCovarianceMCMC.needs_initial_phase()
AdaptiveCovarianceMCMC.needs_sensitivities()
AdaptiveCovarianceMCMC.replace()
AdaptiveCovarianceMCMC.set_eta()
AdaptiveCovarianceMCMC.set_hyper_parameters()
AdaptiveCovarianceMCMC.set_initial_phase()
AdaptiveCovarianceMCMC.set_target_acceptance_rate()
AdaptiveCovarianceMCMC.target_acceptance_rate()
AdaptiveCovarianceMCMC.tell()
- Hamiltonian MCMC
HamiltonianMCMC
HamiltonianMCMC.ask()
HamiltonianMCMC.divergent_iterations()
HamiltonianMCMC.epsilon()
HamiltonianMCMC.hamiltonian_threshold()
HamiltonianMCMC.in_initial_phase()
HamiltonianMCMC.leapfrog_step_size()
HamiltonianMCMC.leapfrog_steps()
HamiltonianMCMC.n_hyper_parameters()
HamiltonianMCMC.name()
HamiltonianMCMC.needs_initial_phase()
HamiltonianMCMC.needs_sensitivities()
HamiltonianMCMC.replace()
HamiltonianMCMC.scaled_epsilon()
HamiltonianMCMC.set_epsilon()
HamiltonianMCMC.set_hamiltonian_threshold()
HamiltonianMCMC.set_hyper_parameters()
HamiltonianMCMC.set_initial_phase()
HamiltonianMCMC.set_leapfrog_step_size()
HamiltonianMCMC.set_leapfrog_steps()
HamiltonianMCMC.tell()
- Metropolis-Adjusted Langevin Algorithm (MALA) MCMC
MALAMCMC
MALAMCMC.acceptance_rate()
MALAMCMC.ask()
MALAMCMC.epsilon()
MALAMCMC.in_initial_phase()
MALAMCMC.n_hyper_parameters()
MALAMCMC.name()
MALAMCMC.needs_initial_phase()
MALAMCMC.needs_sensitivities()
MALAMCMC.replace()
MALAMCMC.set_epsilon()
MALAMCMC.set_hyper_parameters()
MALAMCMC.set_initial_phase()
MALAMCMC.tell()
- Metropolis Random Walk MCMC
MetropolisRandomWalkMCMC
MetropolisRandomWalkMCMC.acceptance_rate()
MetropolisRandomWalkMCMC.ask()
MetropolisRandomWalkMCMC.in_initial_phase()
MetropolisRandomWalkMCMC.n_hyper_parameters()
MetropolisRandomWalkMCMC.name()
MetropolisRandomWalkMCMC.needs_initial_phase()
MetropolisRandomWalkMCMC.needs_sensitivities()
MetropolisRandomWalkMCMC.replace()
MetropolisRandomWalkMCMC.set_hyper_parameters()
MetropolisRandomWalkMCMC.set_initial_phase()
MetropolisRandomWalkMCMC.tell()
- Monomial-Gamma Hamiltonian MCMC
MonomialGammaHamiltonianMCMC
MonomialGammaHamiltonianMCMC.a()
MonomialGammaHamiltonianMCMC.ask()
MonomialGammaHamiltonianMCMC.c()
MonomialGammaHamiltonianMCMC.divergent_iterations()
MonomialGammaHamiltonianMCMC.epsilon()
MonomialGammaHamiltonianMCMC.hamiltonian_threshold()
MonomialGammaHamiltonianMCMC.in_initial_phase()
MonomialGammaHamiltonianMCMC.leapfrog_step_size()
MonomialGammaHamiltonianMCMC.leapfrog_steps()
MonomialGammaHamiltonianMCMC.mass()
MonomialGammaHamiltonianMCMC.n_hyper_parameters()
MonomialGammaHamiltonianMCMC.name()
MonomialGammaHamiltonianMCMC.needs_initial_phase()
MonomialGammaHamiltonianMCMC.needs_sensitivities()
MonomialGammaHamiltonianMCMC.replace()
MonomialGammaHamiltonianMCMC.scaled_epsilon()
MonomialGammaHamiltonianMCMC.set_a()
MonomialGammaHamiltonianMCMC.set_c()
MonomialGammaHamiltonianMCMC.set_epsilon()
MonomialGammaHamiltonianMCMC.set_hamiltonian_threshold()
MonomialGammaHamiltonianMCMC.set_hyper_parameters()
MonomialGammaHamiltonianMCMC.set_initial_phase()
MonomialGammaHamiltonianMCMC.set_leapfrog_step_size()
MonomialGammaHamiltonianMCMC.set_leapfrog_steps()
MonomialGammaHamiltonianMCMC.set_mass()
MonomialGammaHamiltonianMCMC.tell()
- No-U-Turn MCMC Sampler
NoUTurnMCMC
NoUTurnMCMC.ask()
NoUTurnMCMC.delta()
NoUTurnMCMC.divergent_iterations()
NoUTurnMCMC.hamiltonian_threshold()
NoUTurnMCMC.in_initial_phase()
NoUTurnMCMC.load_state()
NoUTurnMCMC.max_tree_depth()
NoUTurnMCMC.n_hyper_parameters()
NoUTurnMCMC.name()
NoUTurnMCMC.needs_initial_phase()
NoUTurnMCMC.needs_sensitivities()
NoUTurnMCMC.number_adaption_steps()
NoUTurnMCMC.replace()
NoUTurnMCMC.save_state()
NoUTurnMCMC.set_delta()
NoUTurnMCMC.set_hamiltonian_threshold()
NoUTurnMCMC.set_hyper_parameters()
NoUTurnMCMC.set_initial_phase()
NoUTurnMCMC.set_max_tree_depth()
NoUTurnMCMC.set_number_adaption_steps()
NoUTurnMCMC.set_use_dense_mass_matrix()
NoUTurnMCMC.tell()
NoUTurnMCMC.use_dense_mass_matrix()
- Population MCMC
PopulationMCMC
PopulationMCMC.ask()
PopulationMCMC.in_initial_phase()
PopulationMCMC.n_hyper_parameters()
PopulationMCMC.name()
PopulationMCMC.needs_initial_phase()
PopulationMCMC.needs_sensitivities()
PopulationMCMC.replace()
PopulationMCMC.set_hyper_parameters()
PopulationMCMC.set_initial_phase()
PopulationMCMC.set_temperature_schedule()
PopulationMCMC.tell()
PopulationMCMC.temperature_schedule()
- Rao-Blackwell ACMC
RaoBlackwellACMC
RaoBlackwellACMC.acceptance_rate()
RaoBlackwellACMC.ask()
RaoBlackwellACMC.eta()
RaoBlackwellACMC.in_initial_phase()
RaoBlackwellACMC.n_hyper_parameters()
RaoBlackwellACMC.name()
RaoBlackwellACMC.needs_initial_phase()
RaoBlackwellACMC.needs_sensitivities()
RaoBlackwellACMC.replace()
RaoBlackwellACMC.set_eta()
RaoBlackwellACMC.set_hyper_parameters()
RaoBlackwellACMC.set_initial_phase()
RaoBlackwellACMC.set_target_acceptance_rate()
RaoBlackwellACMC.target_acceptance_rate()
RaoBlackwellACMC.tell()
- Relativistic MCMC
RelativisticMCMC
RelativisticMCMC.ask()
RelativisticMCMC.divergent_iterations()
RelativisticMCMC.epsilon()
RelativisticMCMC.hamiltonian_threshold()
RelativisticMCMC.in_initial_phase()
RelativisticMCMC.leapfrog_step_size()
RelativisticMCMC.leapfrog_steps()
RelativisticMCMC.mass()
RelativisticMCMC.n_hyper_parameters()
RelativisticMCMC.name()
RelativisticMCMC.needs_initial_phase()
RelativisticMCMC.needs_sensitivities()
RelativisticMCMC.replace()
RelativisticMCMC.scaled_epsilon()
RelativisticMCMC.set_epsilon()
RelativisticMCMC.set_hamiltonian_threshold()
RelativisticMCMC.set_hyper_parameters()
RelativisticMCMC.set_initial_phase()
RelativisticMCMC.set_leapfrog_step_size()
RelativisticMCMC.set_leapfrog_steps()
RelativisticMCMC.set_mass()
RelativisticMCMC.set_speed_of_light()
RelativisticMCMC.speed_of_light()
RelativisticMCMC.tell()
- Slice Sampling - Doubling MCMC
SliceDoublingMCMC
SliceDoublingMCMC.ask()
SliceDoublingMCMC.current_slice_height()
SliceDoublingMCMC.expansion_steps()
SliceDoublingMCMC.in_initial_phase()
SliceDoublingMCMC.n_hyper_parameters()
SliceDoublingMCMC.name()
SliceDoublingMCMC.needs_initial_phase()
SliceDoublingMCMC.needs_sensitivities()
SliceDoublingMCMC.replace()
SliceDoublingMCMC.set_expansion_steps()
SliceDoublingMCMC.set_hyper_parameters()
SliceDoublingMCMC.set_initial_phase()
SliceDoublingMCMC.set_width()
SliceDoublingMCMC.tell()
SliceDoublingMCMC.width()
- Slice Sampling - Rank Shrinking MCMC
SliceRankShrinkingMCMC
SliceRankShrinkingMCMC.ask()
SliceRankShrinkingMCMC.current_slice_height()
SliceRankShrinkingMCMC.in_initial_phase()
SliceRankShrinkingMCMC.n_hyper_parameters()
SliceRankShrinkingMCMC.name()
SliceRankShrinkingMCMC.needs_initial_phase()
SliceRankShrinkingMCMC.needs_sensitivities()
SliceRankShrinkingMCMC.replace()
SliceRankShrinkingMCMC.set_hyper_parameters()
SliceRankShrinkingMCMC.set_initial_phase()
SliceRankShrinkingMCMC.set_sigma_c()
SliceRankShrinkingMCMC.sigma_c()
SliceRankShrinkingMCMC.tell()
- Slice Sampling - Stepout MCMC
SliceStepoutMCMC
SliceStepoutMCMC.ask()
SliceStepoutMCMC.bisection_steps()
SliceStepoutMCMC.current_slice_height()
SliceStepoutMCMC.expansion_steps()
SliceStepoutMCMC.in_initial_phase()
SliceStepoutMCMC.n_hyper_parameters()
SliceStepoutMCMC.name()
SliceStepoutMCMC.needs_initial_phase()
SliceStepoutMCMC.needs_sensitivities()
SliceStepoutMCMC.prob_overrelaxed()
SliceStepoutMCMC.replace()
SliceStepoutMCMC.set_bisection_steps()
SliceStepoutMCMC.set_expansion_steps()
SliceStepoutMCMC.set_hyper_parameters()
SliceStepoutMCMC.set_initial_phase()
SliceStepoutMCMC.set_prob_overrelaxed()
SliceStepoutMCMC.set_width()
SliceStepoutMCMC.tell()
SliceStepoutMCMC.width()
- MCMC Summary
- Running an MCMC routine
- Nested samplers
- Nested sampler base class
NestedSampler
NestedSampler.active_points()
NestedSampler.ask()
NestedSampler.in_initial_phase()
NestedSampler.min_index()
NestedSampler.n_active_points()
NestedSampler.n_hyper_parameters()
NestedSampler.name()
NestedSampler.needs_initial_phase()
NestedSampler.needs_sensitivities()
NestedSampler.running_log_likelihood()
NestedSampler.set_hyper_parameters()
NestedSampler.set_initial_phase()
NestedSampler.set_n_active_points()
NestedSampler.tell()
NestedController
NestedController.active_points()
NestedController.effective_sample_size()
NestedController.inactive_points()
NestedController.iterations()
NestedController.log_likelihood_vector()
NestedController.marginal_log_likelihood()
NestedController.marginal_log_likelihood_standard_deviation()
NestedController.marginal_log_likelihood_threshold()
NestedController.n_posterior_samples()
NestedController.parallel()
NestedController.posterior_samples()
NestedController.prior_space()
NestedController.run()
NestedController.sample_from_posterior()
NestedController.set_iterations()
NestedController.set_log_to_file()
NestedController.set_log_to_screen()
NestedController.set_marginal_log_likelihood_threshold()
NestedController.set_n_posterior_samples()
NestedController.set_parallel()
NestedController.time()
- Nested ellipsoid sampler
NestedEllipsoidSampler
NestedEllipsoidSampler.active_points()
NestedEllipsoidSampler.alpha()
NestedEllipsoidSampler.ask()
NestedEllipsoidSampler.dynamic_enlargement_factor()
NestedEllipsoidSampler.ellipsoid_update_gap()
NestedEllipsoidSampler.enlargement_factor()
NestedEllipsoidSampler.in_initial_phase()
NestedEllipsoidSampler.min_index()
NestedEllipsoidSampler.n_active_points()
NestedEllipsoidSampler.n_hyper_parameters()
NestedEllipsoidSampler.n_rejection_samples()
NestedEllipsoidSampler.name()
NestedEllipsoidSampler.needs_initial_phase()
NestedEllipsoidSampler.needs_sensitivities()
NestedEllipsoidSampler.running_log_likelihood()
NestedEllipsoidSampler.set_alpha()
NestedEllipsoidSampler.set_dynamic_enlargement_factor()
NestedEllipsoidSampler.set_ellipsoid_update_gap()
NestedEllipsoidSampler.set_enlargement_factor()
NestedEllipsoidSampler.set_hyper_parameters()
NestedEllipsoidSampler.set_initial_phase()
NestedEllipsoidSampler.set_n_active_points()
NestedEllipsoidSampler.set_n_rejection_samples()
NestedEllipsoidSampler.tell()
- Nested rejection sampler
NestedRejectionSampler
NestedRejectionSampler.active_points()
NestedRejectionSampler.ask()
NestedRejectionSampler.in_initial_phase()
NestedRejectionSampler.min_index()
NestedRejectionSampler.n_active_points()
NestedRejectionSampler.n_hyper_parameters()
NestedRejectionSampler.name()
NestedRejectionSampler.needs_initial_phase()
NestedRejectionSampler.needs_sensitivities()
NestedRejectionSampler.running_log_likelihood()
NestedRejectionSampler.set_hyper_parameters()
NestedRejectionSampler.set_initial_phase()
NestedRejectionSampler.set_n_active_points()
NestedRejectionSampler.tell()
- Nested sampler base class
- Noise generators
- Optimisers
- Running an optimisation
optimise()
OptimisationController
OptimisationController.evaluations()
OptimisationController.f_guessed_tracking()
OptimisationController.iterations()
OptimisationController.max_evaluations()
OptimisationController.max_iterations()
OptimisationController.max_unchanged_iterations()
OptimisationController.optimiser()
OptimisationController.parallel()
OptimisationController.run()
OptimisationController.set_callback()
OptimisationController.set_f_guessed_tracking()
OptimisationController.set_log_interval()
OptimisationController.set_log_to_file()
OptimisationController.set_log_to_screen()
OptimisationController.set_max_evaluations()
OptimisationController.set_max_iterations()
OptimisationController.set_max_unchanged_iterations()
OptimisationController.set_parallel()
OptimisationController.set_threshold()
OptimisationController.threshold()
OptimisationController.time()
Optimisation
Optimisation.evaluations()
Optimisation.f_guessed_tracking()
Optimisation.iterations()
Optimisation.max_evaluations()
Optimisation.max_iterations()
Optimisation.max_unchanged_iterations()
Optimisation.optimiser()
Optimisation.parallel()
Optimisation.run()
Optimisation.set_callback()
Optimisation.set_f_guessed_tracking()
Optimisation.set_log_interval()
Optimisation.set_log_to_file()
Optimisation.set_log_to_screen()
Optimisation.set_max_evaluations()
Optimisation.set_max_iterations()
Optimisation.set_max_unchanged_iterations()
Optimisation.set_parallel()
Optimisation.set_threshold()
Optimisation.threshold()
Optimisation.time()
- Optimiser base classes
Optimiser
Optimiser.ask()
Optimiser.f_best()
Optimiser.f_guessed()
Optimiser.fbest()
Optimiser.n_hyper_parameters()
Optimiser.name()
Optimiser.needs_sensitivities()
Optimiser.running()
Optimiser.set_hyper_parameters()
Optimiser.stop()
Optimiser.tell()
Optimiser.x_best()
Optimiser.x_guessed()
Optimiser.xbest()
PopulationBasedOptimiser
PopulationBasedOptimiser.ask()
PopulationBasedOptimiser.f_best()
PopulationBasedOptimiser.f_guessed()
PopulationBasedOptimiser.fbest()
PopulationBasedOptimiser.n_hyper_parameters()
PopulationBasedOptimiser.name()
PopulationBasedOptimiser.needs_sensitivities()
PopulationBasedOptimiser.population_size()
PopulationBasedOptimiser.running()
PopulationBasedOptimiser.set_hyper_parameters()
PopulationBasedOptimiser.set_population_size()
PopulationBasedOptimiser.stop()
PopulationBasedOptimiser.suggested_population_size()
PopulationBasedOptimiser.tell()
PopulationBasedOptimiser.x_best()
PopulationBasedOptimiser.x_guessed()
PopulationBasedOptimiser.xbest()
- Convenience methods
- Adam (adaptive moment estimation)
- Bare-bones CMA-ES
BareCMAES
BareCMAES.ask()
BareCMAES.cov()
BareCMAES.f_best()
BareCMAES.f_guessed()
BareCMAES.fbest()
BareCMAES.mean()
BareCMAES.n_hyper_parameters()
BareCMAES.name()
BareCMAES.needs_sensitivities()
BareCMAES.population_size()
BareCMAES.running()
BareCMAES.set_hyper_parameters()
BareCMAES.set_population_size()
BareCMAES.stop()
BareCMAES.suggested_population_size()
BareCMAES.tell()
BareCMAES.x_best()
BareCMAES.x_guessed()
BareCMAES.xbest()
- CMA-ES
CMAES
CMAES.ask()
CMAES.f_best()
CMAES.f_guessed()
CMAES.fbest()
CMAES.n_hyper_parameters()
CMAES.name()
CMAES.needs_sensitivities()
CMAES.population_size()
CMAES.running()
CMAES.set_hyper_parameters()
CMAES.set_population_size()
CMAES.stop()
CMAES.suggested_population_size()
CMAES.tell()
CMAES.x_best()
CMAES.x_guessed()
CMAES.xbest()
- Gradient descent (fixed learning rate)
GradientDescent
GradientDescent.ask()
GradientDescent.f_best()
GradientDescent.f_guessed()
GradientDescent.fbest()
GradientDescent.learning_rate()
GradientDescent.n_hyper_parameters()
GradientDescent.name()
GradientDescent.needs_sensitivities()
GradientDescent.running()
GradientDescent.set_hyper_parameters()
GradientDescent.set_learning_rate()
GradientDescent.stop()
GradientDescent.tell()
GradientDescent.x_best()
GradientDescent.x_guessed()
GradientDescent.xbest()
- Improved Rprop- (iRprop-)
IRPropMin
IRPropMin.ask()
IRPropMin.f_best()
IRPropMin.f_guessed()
IRPropMin.fbest()
IRPropMin.max_step_size()
IRPropMin.min_step_size()
IRPropMin.n_hyper_parameters()
IRPropMin.name()
IRPropMin.needs_sensitivities()
IRPropMin.running()
IRPropMin.set_hyper_parameters()
IRPropMin.set_max_step_size()
IRPropMin.set_min_step_size()
IRPropMin.stop()
IRPropMin.tell()
IRPropMin.x_best()
IRPropMin.x_guessed()
IRPropMin.xbest()
- Nelder-Mead
NelderMead
NelderMead.ask()
NelderMead.f_best()
NelderMead.f_guessed()
NelderMead.fbest()
NelderMead.n_hyper_parameters()
NelderMead.name()
NelderMead.needs_sensitivities()
NelderMead.running()
NelderMead.set_hyper_parameters()
NelderMead.stop()
NelderMead.tell()
NelderMead.x_best()
NelderMead.x_guessed()
NelderMead.xbest()
- PSO
PSO
PSO.ask()
PSO.f_best()
PSO.f_guessed()
PSO.fbest()
PSO.n_hyper_parameters()
PSO.name()
PSO.needs_sensitivities()
PSO.population_size()
PSO.running()
PSO.set_hyper_parameters()
PSO.set_local_global_balance()
PSO.set_population_size()
PSO.stop()
PSO.suggested_population_size()
PSO.tell()
PSO.x_best()
PSO.x_guessed()
PSO.xbest()
- SNES
SNES
SNES.ask()
SNES.f_best()
SNES.f_guessed()
SNES.fbest()
SNES.n_hyper_parameters()
SNES.name()
SNES.needs_sensitivities()
SNES.population_size()
SNES.running()
SNES.set_hyper_parameters()
SNES.set_population_size()
SNES.stop()
SNES.suggested_population_size()
SNES.tell()
SNES.x_best()
SNES.x_guessed()
SNES.xbest()
- xNES
XNES
XNES.ask()
XNES.f_best()
XNES.f_guessed()
XNES.fbest()
XNES.n_hyper_parameters()
XNES.name()
XNES.needs_sensitivities()
XNES.population_size()
XNES.running()
XNES.set_hyper_parameters()
XNES.set_population_size()
XNES.stop()
XNES.suggested_population_size()
XNES.tell()
XNES.x_best()
XNES.x_guessed()
XNES.xbest()
- Running an optimisation
- Noise model diagnostics
- Toy problems
- Toy base classes
- Annulus Distribution
- Beeler-Reuter Action Potential Model
ActionPotentialModel
ActionPotentialModel.initial_conditions()
ActionPotentialModel.n_outputs()
ActionPotentialModel.n_parameters()
ActionPotentialModel.set_initial_conditions()
ActionPotentialModel.set_solver_tolerances()
ActionPotentialModel.simulate()
ActionPotentialModel.simulate_all_states()
ActionPotentialModel.suggested_parameters()
ActionPotentialModel.suggested_times()
- Cone Distribution
- Constant Model
- Eight Schools distribution
- Fitzhugh-Nagumo Model
FitzhughNagumoModel
FitzhughNagumoModel.initial_conditions()
FitzhughNagumoModel.jacobian()
FitzhughNagumoModel.n_outputs()
FitzhughNagumoModel.n_parameters()
FitzhughNagumoModel.n_states()
FitzhughNagumoModel.set_initial_conditions()
FitzhughNagumoModel.simulate()
FitzhughNagumoModel.simulateS1()
FitzhughNagumoModel.suggested_parameters()
FitzhughNagumoModel.suggested_times()
- Gaussian distribution
- German Credit Hierarchical Logistic Distribution
- German Credit Logistic Distribution
- Goodwin oscillator model
GoodwinOscillatorModel
GoodwinOscillatorModel.initial_conditions()
GoodwinOscillatorModel.jacobian()
GoodwinOscillatorModel.n_outputs()
GoodwinOscillatorModel.n_parameters()
GoodwinOscillatorModel.n_states()
GoodwinOscillatorModel.set_initial_conditions()
GoodwinOscillatorModel.simulate()
GoodwinOscillatorModel.simulateS1()
GoodwinOscillatorModel.suggested_parameters()
GoodwinOscillatorModel.suggested_times()
- HES1 Michaelis-Menten Model
Hes1Model
Hes1Model.fixed_parameters()
Hes1Model.initial_conditions()
Hes1Model.jacobian()
Hes1Model.m0()
Hes1Model.n_outputs()
Hes1Model.n_parameters()
Hes1Model.n_states()
Hes1Model.set_fixed_parameters()
Hes1Model.set_initial_conditions()
Hes1Model.set_m0()
Hes1Model.simulate()
Hes1Model.simulateS1()
Hes1Model.simulate_all_states()
Hes1Model.suggested_parameters()
Hes1Model.suggested_times()
Hes1Model.suggested_values()
- High dimensional Gaussian distribution
- Hodgkin-Huxley IK Experiment Model
- Logistic model
- Lotka-Volterra model
LotkaVolterraModel
LotkaVolterraModel.initial_conditions()
LotkaVolterraModel.jacobian()
LotkaVolterraModel.n_outputs()
LotkaVolterraModel.n_parameters()
LotkaVolterraModel.n_states()
LotkaVolterraModel.set_initial_conditions()
LotkaVolterraModel.simulate()
LotkaVolterraModel.simulateS1()
LotkaVolterraModel.suggested_parameters()
LotkaVolterraModel.suggested_times()
LotkaVolterraModel.suggested_values()
- Multimodal Gaussian distribution
- Neal’s Funnel Distribution
- Parabolic error
- Repressilator model
- Rosenbrock function
- Simple Egg Box Distribution
- Simple Harmonic Oscillator model
- SIR Epidemiology model
- Twisted Gaussian distribution
- Stochastic Toy Problems
- Markov Jump Model
- Stochastic degradation model
- Stochastic Logistic Model
- Stochastic Michaelis Menten model
- Stochastic production and degradation model
ProductionDegradationModel
ProductionDegradationModel.interpolate_mol_counts()
ProductionDegradationModel.n_outputs()
ProductionDegradationModel.n_parameters()
ProductionDegradationModel.simulate()
ProductionDegradationModel.simulate_raw()
ProductionDegradationModel.suggested_parameters()
ProductionDegradationModel.suggested_times()
- Schlogl’s model
- Transformations
- Transformation types
Transformation
Transformation.convert_boundaries()
Transformation.convert_covariance_matrix()
Transformation.convert_error_measure()
Transformation.convert_log_pdf()
Transformation.convert_log_prior()
Transformation.convert_standard_deviation()
Transformation.elementwise()
Transformation.jacobian()
Transformation.jacobian_S1()
Transformation.log_jacobian_det()
Transformation.log_jacobian_det_S1()
Transformation.n_parameters()
Transformation.to_model()
Transformation.to_search()
ComposedTransformation
ComposedTransformation.convert_boundaries()
ComposedTransformation.convert_covariance_matrix()
ComposedTransformation.convert_error_measure()
ComposedTransformation.convert_log_pdf()
ComposedTransformation.convert_log_prior()
ComposedTransformation.convert_standard_deviation()
ComposedTransformation.elementwise()
ComposedTransformation.jacobian()
ComposedTransformation.jacobian_S1()
ComposedTransformation.log_jacobian_det()
ComposedTransformation.log_jacobian_det_S1()
ComposedTransformation.n_parameters()
ComposedTransformation.to_model()
ComposedTransformation.to_search()
IdentityTransformation
IdentityTransformation.convert_boundaries()
IdentityTransformation.convert_covariance_matrix()
IdentityTransformation.convert_error_measure()
IdentityTransformation.convert_log_pdf()
IdentityTransformation.convert_log_prior()
IdentityTransformation.convert_standard_deviation()
IdentityTransformation.elementwise()
IdentityTransformation.jacobian()
IdentityTransformation.jacobian_S1()
IdentityTransformation.log_jacobian_det()
IdentityTransformation.log_jacobian_det_S1()
IdentityTransformation.n_parameters()
IdentityTransformation.to_model()
IdentityTransformation.to_search()
LogitTransformation
LogitTransformation.convert_boundaries()
LogitTransformation.convert_covariance_matrix()
LogitTransformation.convert_error_measure()
LogitTransformation.convert_log_pdf()
LogitTransformation.convert_log_prior()
LogitTransformation.convert_standard_deviation()
LogitTransformation.elementwise()
LogitTransformation.jacobian()
LogitTransformation.jacobian_S1()
LogitTransformation.log_jacobian_det()
LogitTransformation.log_jacobian_det_S1()
LogitTransformation.n_parameters()
LogitTransformation.to_model()
LogitTransformation.to_search()
LogTransformation
LogTransformation.convert_boundaries()
LogTransformation.convert_covariance_matrix()
LogTransformation.convert_error_measure()
LogTransformation.convert_log_pdf()
LogTransformation.convert_log_prior()
LogTransformation.convert_standard_deviation()
LogTransformation.elementwise()
LogTransformation.jacobian()
LogTransformation.jacobian_S1()
LogTransformation.log_jacobian_det()
LogTransformation.log_jacobian_det_S1()
LogTransformation.n_parameters()
LogTransformation.to_model()
LogTransformation.to_search()
RectangularBoundariesTransformation
RectangularBoundariesTransformation.convert_boundaries()
RectangularBoundariesTransformation.convert_covariance_matrix()
RectangularBoundariesTransformation.convert_error_measure()
RectangularBoundariesTransformation.convert_log_pdf()
RectangularBoundariesTransformation.convert_log_prior()
RectangularBoundariesTransformation.convert_standard_deviation()
RectangularBoundariesTransformation.elementwise()
RectangularBoundariesTransformation.jacobian()
RectangularBoundariesTransformation.jacobian_S1()
RectangularBoundariesTransformation.log_jacobian_det()
RectangularBoundariesTransformation.log_jacobian_det_S1()
RectangularBoundariesTransformation.n_parameters()
RectangularBoundariesTransformation.to_model()
RectangularBoundariesTransformation.to_search()
ScalingTransformation
ScalingTransformation.convert_boundaries()
ScalingTransformation.convert_covariance_matrix()
ScalingTransformation.convert_error_measure()
ScalingTransformation.convert_log_pdf()
ScalingTransformation.convert_log_prior()
ScalingTransformation.convert_standard_deviation()
ScalingTransformation.elementwise()
ScalingTransformation.jacobian()
ScalingTransformation.jacobian_S1()
ScalingTransformation.log_jacobian_det()
ScalingTransformation.log_jacobian_det_S1()
ScalingTransformation.n_parameters()
ScalingTransformation.to_model()
ScalingTransformation.to_search()
UnitCubeTransformation
UnitCubeTransformation.convert_boundaries()
UnitCubeTransformation.convert_covariance_matrix()
UnitCubeTransformation.convert_error_measure()
UnitCubeTransformation.convert_log_pdf()
UnitCubeTransformation.convert_log_prior()
UnitCubeTransformation.convert_standard_deviation()
UnitCubeTransformation.elementwise()
UnitCubeTransformation.jacobian()
UnitCubeTransformation.jacobian_S1()
UnitCubeTransformation.log_jacobian_det()
UnitCubeTransformation.log_jacobian_det_S1()
UnitCubeTransformation.n_parameters()
UnitCubeTransformation.to_model()
UnitCubeTransformation.to_search()
- Transformed objects
- Transformation types
- Utilities
Hierarchy of methods¶
Pints contains different types of methods, that can be roughly arranged into a hierarchy, as follows.
Sampling¶
-
MetropolisRandomWalkMCMC
, works on anyLogPDF
.Metropolis-Hastings
Adaptive methods
AdaptiveCovarianceMC
, works on anyLogPDF
.
PopulationMCMC
, works on anyLogPDF
.Differential evolution methods
DifferentialEvolutionMCMC
, works on anyLogPDF
.EmceeHammerMCMC
, works on anyLogPDF
.
-
NestedEllipsoidSampler
, requires aLogPDF
and aLogPrior
that can be sampled from.NestedRejectionSampler
, requires aLogPDF
and aLogPrior
that can be sampled from.
Particle based samplers
SMC
-
ABCSMC
, requires aLogPrior
that can be sampled from from and anErrorMeasure
.RejectionABC
, requires aLogPrior
that can be sampled from and anErrorMeasure
.
1st order sensitivity MCMC samplers (Need derivatives of
LogPDF
)Metropolis-Adjusted Langevin Algorithm (MALA)
, works on anyLogPDF
that provides 1st order sensitivities.Hamiltonian Monte Carlo
, works on anyLogPDF
that provides 1st order sensitivities.NUTS
Differential geometric methods (Need Hessian of
LogPDF
)smMALA
RMHMC
Optimisation¶
All methods shown here are derivative-free methods that work on any
ErrorMeasure
or LogPDF
.
Problems in Pints¶
Pints defines single
and
multi-output
problem classes that wrap around
models and data, and over which error measures
or
log-likelihoods
can be defined.
To find the appropriate type of Problem to use, see the overview below:
Systems with a single observable output
Single data set: Use a
SingleOutputProblem
and any of the appropriate error measures or log-likelihoodsMultiple, independent data sets: Define multiple
SingleOutputProblems
and an error measure / log-likelihood on each, and then combine using e.g.SumOfErrors
orSumOfIndependentLogPDFs
.
Systems with multiple observable outputs
Single data set: Use a
MultiOutputProblem
and any of the appropriate error measures or log-likelihoods