Index

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__init__() (probpy.density.nonparametric_convolution.RCKD method) (probpy.density.nonparametric_convolution.UCKD method) (probpy.density.radial_basis.URBK method)

B

Bernoulli (class in probpy.distributions.bernoulli)

Beta (class in probpy.distributions.beta) Binomial (class in probpy.distributions.binomial)

C

Categorical (class in probpy.distributions.categorical)

D

Dirichlet (class in probpy.distributions.dirichlet)

E

epsilon (probpy.density.radial_basis.URBK attribute)

expected_value() (in module probpy.integration) Exponential (class in probpy.distributions.exponential)

F

fast_metropolis_hastings() (in module probpy.sampling.mcmc) fast_metropolis_hastings_log_space() (in module probpy.sampling.mcmc) fit() (probpy.density.nonparametric_convolution.RCKD method) (probpy.density.nonparametric_convolution.UCKD method) (probpy.density.radial_basis.URBK method)

Function (class in probpy.distributions.function)

G

Gamma (class in probpy.distributions.gamma)

GaussianProcess (class in probpy.distributions.gaussian_process) Geometric (class in probpy.distributions.geometric)

H

Hypergeometric (class in probpy.distributions.hypergeometric)

M

mcmc() (in module probpy.learn.posterior.mcmc) med() (probpy.distributions.bernoulli.Bernoulli class method) (probpy.distributions.beta.Beta class method) (probpy.distributions.binomial.Binomial class method) (probpy.distributions.categorical.Categorical class method) (probpy.distributions.dirichlet.Dirichlet class method) (probpy.distributions.exponential.Exponential class method) (probpy.distributions.function.Function class method) (probpy.distributions.gamma.Gamma class method) (probpy.distributions.gaussian_process.GaussianProcess class method) (probpy.distributions.geometric.Geometric class method) (probpy.distributions.hypergeometric.Hypergeometric class method) (probpy.distributions.multinomial.Multinomial class method) (probpy.distributions.normal.MultiVariateNormal class method) (probpy.distributions.normal.Normal class method) (probpy.distributions.normal_inverse_gamma.NormalInverseGamma class method) (probpy.distributions.points.Points class method) (probpy.distributions.poisson.Poisson class method) (probpy.distributions.uniform.MultiVariateUniform class method) (probpy.distributions.uniform.Uniform class method) (probpy.distributions.unilinear.UniLinear class method)

metropolis() (in module probpy.sampling.mcmc) metropolis_hastings() (in module probpy.sampling.mcmc) mode() (in module probpy.inference.mode) Multinomial (class in probpy.distributions.multinomial) MultiVariateNormal (class in probpy.distributions.normal) MultiVariateUniform (class in probpy.distributions.uniform)

N

Normal (class in probpy.distributions.normal)

NormalInverseGamma (class in probpy.distributions.normal_inverse_gamma)

P

p (probpy.distributions.binomial.Binomial attribute) p() (probpy.density.nonparametric_convolution.RCKD method) (probpy.density.nonparametric_convolution.UCKD method) (probpy.density.radial_basis.URBK method) (probpy.distributions.bernoulli.Bernoulli static method) (probpy.distributions.beta.Beta static method) (probpy.distributions.categorical.Categorical static method) (probpy.distributions.dirichlet.Dirichlet static method) (probpy.distributions.exponential.Exponential static method) (probpy.distributions.gamma.Gamma static method) (probpy.distributions.gaussian_process.GaussianProcess static method) (probpy.distributions.normal.Normal static method) parameter_posterior() (in module probpy.learn.posterior.posterior) Points (class in probpy.distributions.points) Poisson (class in probpy.distributions.poisson) probpy.distributions.bernoulli (module) probpy.distributions.beta (module) probpy.distributions.binomial (module) probpy.distributions.categorical (module) probpy.distributions.dirichlet (module)

probpy.distributions.exponential (module) probpy.distributions.function (module) probpy.distributions.gamma (module) probpy.distributions.gaussian_process (module) probpy.distributions.generic (module) probpy.distributions.geometric (module) probpy.distributions.hypergeometric (module) probpy.distributions.multinomial (module) probpy.distributions.normal (module) probpy.distributions.normal_inverse_gamma (module) probpy.distributions.points (module) probpy.distributions.poisson (module) probpy.distributions.uniform (module) probpy.distributions.unilinear (module) probpy.inference.mode (module) probpy.integration (module) probpy.learn.posterior.mcmc (module) probpy.learn.posterior.posterior (module) probpy.learn.posterior.search (module) probpy.sampling.mcmc (module)

R

RCKD (class in probpy.density.nonparametric_convolution)

S

sample (probpy.distributions.binomial.Binomial attribute) (probpy.distributions.categorical.Categorical attribute) (probpy.distributions.normal.MultiVariateNormal attribute) (probpy.distributions.normal.Normal attribute) sample() (probpy.distributions.bernoulli.Bernoulli static method) (probpy.distributions.beta.Beta static method) (probpy.distributions.dirichlet.Dirichlet static method) (probpy.distributions.exponential.Exponential static method) (probpy.distributions.gamma.Gamma static method) (probpy.distributions.gaussian_process.GaussianProcess static method)

search() (in module probpy.learn.posterior.search)

U

UCKD (class in probpy.density.nonparametric_convolution) Uniform (class in probpy.distributions.uniform)

uniform_importance_sampling() (in module probpy.integration) UniLinear (class in probpy.distributions.unilinear) URBK (class in probpy.density.radial_basis)