Index A | B | C | E | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | Z A a (boax.core.distributions.beta.Beta attribute) (boax.core.distributions.gamma.Gamma attribute) (boax.core.distributions.uniform.Uniform attribute) Acquisition (class in boax.acquisitions) additive() (in module boax.core.models.kernels.transformations) B b (boax.core.distributions.beta.Beta attribute) (boax.core.distributions.gamma.Gamma attribute) (boax.core.distributions.uniform.Uniform attribute) bandit() (in module boax.experiments) batch() (in module boax.core.optimizers) Belief (class in boax.policies.believes) Beta (class in boax.core.distributions.beta) beta() (in module boax.core.distributions.beta) (in module boax.core.models.likelihoods) binary() (in module boax.policies.believes) boltzmann() (in module boax.policies) C cdf() (in module boax.core.distributions.beta) (in module boax.core.distributions.gamma) (in module boax.core.distributions.normal) (in module boax.core.distributions.poisson) constant() (in module boax.core.models.means) continuous() (in module boax.policies.believes) cov (boax.core.distributions.multivariate_normal.MultivariateNormal attribute) E epsilon_greedy() (in module boax.policies) exact() (in module boax.core.models.gaussian_process) expected_improvement() (in module boax.acquisitions) G Gamma (class in boax.core.distributions.gamma) gamma() (in module boax.core.distributions.gamma) gaussian() (in module boax.core.models.likelihoods) H halton_normal() (in module boax.core.samplers) halton_uniform() (in module boax.core.samplers) I Initializer (class in boax.core.optimizers.initializers) J joined() (in module boax.core.models.transformations) K Kernel (class in boax.core.models.kernels) L Likelihood (class in boax.core.models.likelihoods) linear() (in module boax.core.models.means) loc (boax.core.distributions.normal.Normal attribute) log_expected_improvement() (in module boax.acquisitions) log_probability_of_improvement() (in module boax.acquisitions) logcdf() (in module boax.core.distributions.beta) (in module boax.core.distributions.gamma) (in module boax.core.distributions.normal) logpdf() (in module boax.core.distributions.beta) (in module boax.core.distributions.gamma) (in module boax.core.distributions.multivariate_normal) (in module boax.core.distributions.normal) (in module boax.core.distributions.uniform) logpmf() (in module boax.core.distributions.poisson) logsf() (in module boax.core.distributions.beta) (in module boax.core.distributions.gamma) (in module boax.core.distributions.normal) M matern_five_halves() (in module boax.core.models.kernels) matern_one_half() (in module boax.core.models.kernels) matern_three_halves() (in module boax.core.models.kernels) mean (boax.core.distributions.multivariate_normal.MultivariateNormal attribute) Mean (class in boax.core.models.means) Model (class in boax.core.models) mu (boax.core.distributions.poisson.Poisson attribute) multivariate_normal() (in module boax.core.distributions.multivariate_normal) MultivariateNormal (class in boax.core.distributions.multivariate_normal) N negative_log_likelihood() (in module boax.core.objectives) Normal (class in boax.core.distributions.normal) normal() (in module boax.core.distributions.normal) (in module boax.core.samplers) O Objective (class in boax.core.objectives), [1] optimization() (in module boax.experiments) Optimizer (class in boax.core.optimizers) P pdf() (in module boax.core.distributions.beta) (in module boax.core.distributions.gamma) (in module boax.core.distributions.multivariate_normal) (in module boax.core.distributions.normal) (in module boax.core.distributions.uniform) penalized() (in module boax.core.objectives.transformations) pmf() (in module boax.core.distributions.poisson) Poisson (class in boax.core.distributions.poisson) poisson() (in module boax.core.distributions.poisson) Policy (class in boax.policies) probability_of_improvement() (in module boax.acquisitions) product() (in module boax.core.models.kernels.transformations) Q q_batch() (in module boax.core.optimizers.initializers) q_batch_nonnegative() (in module boax.core.optimizers.initializers) q_expected_improvement() (in module boax.acquisitions) q_probability_of_improvement() (in module boax.acquisitions) q_upper_confidence_bound() (in module boax.acquisitions) R rbf() (in module boax.core.models.kernels) S Sampler (class in boax.core.samplers) scale (boax.core.distributions.normal.Normal attribute) scaled() (in module boax.core.models.kernels.transformations) scipy() (in module boax.core.optimizers.solvers) sequential() (in module boax.core.optimizers) sf() (in module boax.core.distributions.beta) (in module boax.core.distributions.gamma) (in module boax.core.distributions.normal) single_task_gaussian_process() (in module boax.acquisitions.surrogates) Solver (class in boax.core.optimizers.solvers) Surrogate (class in boax.acquisitions.surrogates) T thompson_sampling() (in module boax.policies) transformed() (in module boax.core.models.transformations) U Uniform (class in boax.core.distributions.uniform) uniform() (in module boax.core.distributions.uniform) (in module boax.core.samplers) upper_confidence_bound() (in module boax.acquisitions) (in module boax.policies) Z zero() (in module boax.core.models.means)