Index

A

AdaptiveMarkovChain (class in pypmc.sampler.markov_chain)

append() (pypmc.tools.History method)

B

ball() (in module pypmc.tools.indicator)

C

calculate_covariance() (in module pypmc.sampler.importance_sampling) calculate_expectation() (in module pypmc.sampler.importance_sampling) calculate_mean() (in module pypmc.sampler.importance_sampling)

clear() (pypmc.tools.History method) combine_weights() (in module pypmc.sampler.importance_sampling) create_gaussian_mixture() (in module pypmc.density.mixture) create_t_mixture() (in module pypmc.density.mixture)

D

Dirichlet_log_C() (in module pypmc.mix_adapt.variational)

E

E_step() (pypmc.mix_adapt.variational.GaussianInference method) ess() (in module pypmc.tools.convergence) evaluate() (pypmc.density.base.LocalDensity method) (pypmc.density.base.ProbabilityDensity method) (pypmc.density.gauss.Gauss method) (pypmc.density.gauss.LocalGauss method) (pypmc.density.mixture.MixtureDensity method) (pypmc.density.student_t.LocalStudentT method) (pypmc.density.student_t.StudentT method)

G

Gauss (class in pypmc.density.gauss)

gaussian_pmc() (in module pypmc.mix_adapt.pmc) GaussianInference (class in pypmc.mix_adapt.variational)

H

Hierarchical (class in pypmc.mix_adapt.hierarchical)

History (class in pypmc.tools) hyperrectangle() (in module pypmc.tools.indicator)

I

ImportanceSampler (class in pypmc.sampler.importance_sampling)

K

kullback_leibler() (in module pypmc.mix_adapt.hierarchical)

L

likelihood_bound() (pypmc.mix_adapt.variational.GaussianInference method) LocalDensity (class in pypmc.density.base)

LocalGauss (class in pypmc.density.gauss) LocalStudentT (class in pypmc.density.student_t) log_likelihood() (pypmc.mix_adapt.pmc.PMC method)

M

M_step() (pypmc.mix_adapt.variational.GaussianInference method) make_mixture() (pypmc.mix_adapt.variational.GaussianInference method) make_r_gaussmix() (in module pypmc.mix_adapt.r_value) make_r_tmix() (in module pypmc.mix_adapt.r_value) MarkovChain (class in pypmc.sampler.markov_chain) merge_function_with_indicator() (in module pypmc.tools.indicator) MixtureDensity (class in pypmc.density.mixture) module pypmc.density pypmc.density.base pypmc.density.gauss pypmc.density.mixture pypmc.density.student_t pypmc.mix_adapt pypmc.mix_adapt.hierarchical pypmc.mix_adapt.pmc pypmc.mix_adapt.r_value pypmc.mix_adapt.variational pypmc.sampler pypmc.sampler.importance_sampling pypmc.sampler.markov_chain pypmc.tools pypmc.tools.convergence pypmc.tools.indicator pypmc.tools.parallel_sampler

MPISampler (class in pypmc.tools.parallel_sampler) multi_evaluate() (pypmc.density.base.ProbabilityDensity method) (pypmc.density.gauss.Gauss method) (pypmc.density.mixture.MixtureDensity method) (pypmc.density.student_t.StudentT method)

N

normalize() (pypmc.density.mixture.MixtureDensity method)

normalized() (pypmc.density.mixture.MixtureDensity method)

P

partition() (in module pypmc.tools) patch_data() (in module pypmc.tools) perp() (in module pypmc.tools.convergence) plot_mixture() (in module pypmc.tools) plot_responsibility() (in module pypmc.tools) PMC (class in pypmc.mix_adapt.pmc) posterior2prior() (pypmc.mix_adapt.variational.GaussianInference method) prior_posterior() (pypmc.mix_adapt.variational.GaussianInference method) ProbabilityDensity (class in pypmc.density.base) propose() (pypmc.density.base.LocalDensity method) (pypmc.density.base.ProbabilityDensity method) (pypmc.density.gauss.Gauss method) (pypmc.density.gauss.LocalGauss method) (pypmc.density.mixture.MixtureDensity method) (pypmc.density.student_t.LocalStudentT method) (pypmc.density.student_t.StudentT method) prune() (pypmc.density.mixture.MixtureDensity method) (pypmc.mix_adapt.variational.GaussianInference method) pypmc.density module pypmc.density.base module pypmc.density.gauss module pypmc.density.mixture module

pypmc.density.student_t module pypmc.mix_adapt module pypmc.mix_adapt.hierarchical module pypmc.mix_adapt.pmc module pypmc.mix_adapt.r_value module pypmc.mix_adapt.variational module pypmc.sampler module pypmc.sampler.importance_sampling module pypmc.sampler.markov_chain module pypmc.tools module pypmc.tools.convergence module pypmc.tools.indicator module pypmc.tools.parallel_sampler module

R

r_group() (in module pypmc.mix_adapt.r_value) r_value() (in module pypmc.mix_adapt.r_value) recover_gaussian_mixture() (in module pypmc.density.mixture)

recover_t_mixture() (in module pypmc.density.mixture) run() (pypmc.mix_adapt.hierarchical.Hierarchical method) (pypmc.mix_adapt.pmc.PMC method) (pypmc.mix_adapt.variational.GaussianInference method)

S

set_variational_parameters() (pypmc.mix_adapt.variational.GaussianInference method)

student_t_pmc() (in module pypmc.mix_adapt.pmc) StudentT (class in pypmc.density.student_t)

U

update() (pypmc.density.gauss.Gauss method) (pypmc.density.gauss.LocalGauss method) (pypmc.density.student_t.StudentT method) (pypmc.mix_adapt.variational.GaussianInference method)

V

VBMerge (class in pypmc.mix_adapt.variational)

W

Wishart_expect_log_lambda() (in module pypmc.mix_adapt.variational)

Wishart_H() (in module pypmc.mix_adapt.variational) Wishart_log_B() (in module pypmc.mix_adapt.variational)