Index

_

__add__() (LogSpace method) (PyTree method) __and__() (TerminationCondition method), [1], [2], [3], [4] __call__() (GlobalOptimisation method), [1] (InterpolatedArray method) (Model method), [1], [2] (NestedSampler method), [1] __contains__() (ScopedDict method) __ge__() (LogSpace method) __getitem__() (LogSpace method) (ScopedDict method) __gt__() (LogSpace method) __hash__() (AbstractModel method) (Model method), [1], [2] __iter__() (ScopedDict method) __le__() (LogSpace method) __len__() (ScopedDict method) __lt__() (LogSpace method) __mul__() (LogSpace method) (PyTree method) __neg__() (LogSpace method) (PyTree method)

__or__() (TerminationCondition method), [1], [2], [3], [4] __post_init__() (EvidenceMaximisation method), [1] (GlobalOptimisation method), [1] (InterpolatedArray method) (MultiDimSliceSampler method), [1] (MultiEllipsoidalSampler method), [1] (NestedSampler method), [1] (ShardedStaticNestedSampler method), [1], [2], [3], [4] (SimpleGlobalOptimisation method), [1] (UniDimSliceSampler method), [1] (UniformSampler method), [1] __pow__() (LogSpace method) (PyTree method) __repr__() (LogSpace method) (Model method), [1], [2] (ScopedDict method) (WrappedTFPDistribution method) __setitem__() (ScopedDict method) __sub__() (LogSpace method) (PyTree method) __truediv__() (LogSpace method) (PyTree method)

A

abs() (LogSpace method) absolute_spread (GlobalOptimisationResults attribute), [1] (GlobalOptimisationState attribute), [1] AbstractDistribution (class in jaxns.framework.abc) AbstractModel (class in jaxns.framework.abc) AbstractNestedSampler (class in jaxns.nested_samplers.abc) AbstractPrior (class in jaxns.framework.abc) AbstractSampler (class in jaxns.samplers.abc) accepted (NewtonDiagnostic attribute)

adaptive_shrink (UniDimSliceSampler attribute), [1] add_chunk_dim() (in module jaxns.internals.maps) analytic_posterior_samples() (in module jaxns) (in module jaxns.utils) apply_interp() (in module jaxns.internals.interp_utils) argmax() (LogSpace method) atol (GlobalOptimisationTerminationCondition attribute), [1] (TerminationCondition attribute), [1], [2], [3], [4] axis (InterpolatedArray attribute)

B

BaseAbstractMarkovSampler (class in jaxns.samplers.bases) BaseAbstractRejectionSampler (class in jaxns.samplers.bases) batch_size (EvidenceMaximisation attribute), [1] Bernoulli (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) Beta (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors)

block_until_ready() (in module jaxns.internals.maps) BoolArray (in module jaxns.internals.types) broadcast_dtypes() (in module jaxns.internals.shapes) broadcast_shapes() (in module jaxns.internals.shapes) bruteforce_evidence() (in module jaxns) (in module jaxns.utils) bruteforce_posterior_samples() (in module jaxns) (in module jaxns.utils) build_hvp() (in module jaxns.experimental.solvers.ad_utils) BUX (in module jaxns.internals.maps)

C

c (NestedSampler attribute), [1] cast_to_count() (Policy method) cast_to_index() (Policy method) cast_to_measure() (Policy method) Categorical (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) cdf_diff (TruncationWrapper attribute), [1], [2] cdf_low (TruncationWrapper attribute), [1], [2] cg_iters (NewtonDiagnostic attribute) cg_solve() (in module jaxns.experimental.solvers.cg) CGDiagnostics (class in jaxns.experimental.solvers.cg) chunked_pmap() (in module jaxns.internals.maps) chunked_vmap() (in module jaxns.internals.maps) cluster_id (MultEllipsoidState attribute) complex_type (in module jaxns.internals.mixed_precision) compute_enclosed_prior_volume() (in module jaxns.internals.shrinkage_statistics) compute_evidence_stats() (in module jaxns.internals.shrinkage_statistics) compute_num_live_points_from_unit_threads() (in module jaxns.internals.tree_structure)

concatenate() (LogSpace method) concatenate_sample_trees() (in module jaxns.internals.tree_structure) convert_external_params() (in module jaxns.framework.context) convert_to_array() (in module jaxns.internals.shapes) convert_to_real() (in module jaxns.experimental.solvers.gauss_newton_cg) count_crossed_edges() (in module jaxns.internals.tree_structure) count_crossed_edges_less_fast() (in module jaxns.internals.tree_structure) count_dtype (Policy attribute) count_intervals_naive() (in module jaxns.internals.tree_structure) count_old() (in module jaxns.internals.tree_structure) create_init_evidence_calc() (in module jaxns.internals.shrinkage_statistics) create_init_state() (in module jaxns.nested_samplers.common.initialisation) create_init_termination_register() (in module jaxns.nested_samplers.common.initialisation) create_mesh() (in module jaxns.internals.maps) CT (in module jaxns.experimental.solvers.gauss_newton_cg) cumprod() (LogSpace method) cumsum() (LogSpace method) cumulative_logsumexp() (in module jaxns.internals.log_semiring) cumulative_op_dynamic() (in module jaxns.internals.cumulative_ops) cumulative_op_static() (in module jaxns.internals.cumulative_ops)

D

damping (NewtonDiagnostic attribute) ddelta_x_norm (NewtonDiagnostic attribute) DefaultGlobalOptimisation (in module jaxns.experimental) (in module jaxns.experimental.public) delta_f_actual (NewtonDiagnostic attribute) delta_f_pred (NewtonDiagnostic attribute) delta_x_norm (NewtonDiagnostic attribute) density_estimation() (in module jaxns.internals.stats) deprecated() (in module jaxns.warnings) depth (MultiEllipsoidalSampler attribute), [1] deserialise_jax_ndarray() (in module jaxns.internals.namedtuple_utils) deserialise_namedtuple() (in module jaxns.internals.namedtuple_utils) deserialise_ndarray() (in module jaxns.internals.namedtuple_utils) determine_termination() (in module jaxns.nested_samplers.common.termination) devices (GlobalOptimisation attribute), [1] (NestedSampler attribute), [1] (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] (SimpleGlobalOptimisation attribute), [1]

dict (ScopedDict attribute) diff() (LogSpace method) difficult_model (NestedSampler attribute), [1] dist (Bernoulli attribute), [1], [2] (Categorical attribute), [1], [2] (Poisson attribute), [1], [2] (Prior property), [1], [2] dist_chain (WrappedTFPDistribution attribute) dlogZ (TerminationCondition attribute), [1], [2], [3], [4] DomainType (in module jaxns.experimental.solvers.cg) (in module jaxns.experimental.solvers.gauss_newton_cg) draw_uniform_samples() (in module jaxns.nested_samplers.common.uniform_sample) dtype (LogSpace property)

E

e_step() (EvidenceMaximisation method), [1] (in module jaxns.samplers.multi_ellipsoid.em_gmm) effective_sample_size_kish() (in module jaxns.internals.stats) efficiency_threshold (TerminationCondition attribute), [1], [2], [3], [4] ellipsoid_clustering() (in module jaxns.samplers.multi_ellipsoid.multi_ellipsoid_utils) em_gmm() (in module jaxns.samplers.multi_ellipsoid.em_gmm) Empirical (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) EphemeralState (class in jaxns.samplers.abc) ESS (NestedSamplerResults attribute), [1], [2], [3], [4]

ess (TerminationCondition attribute), [1], [2], [3], [4] evaluate_map_estimate() (in module jaxns) (in module jaxns.utils) evidence_uncert (TerminationCondition attribute), [1], [2], [3], [4] EvidenceMaximisation (class in jaxns.experimental) (class in jaxns.experimental.evidence_maximisation) EvidenceUpdateVariables (class in jaxns.internals.shrinkage_statistics) exp() (LogSpace method) expansion_factor (MultiEllipsoidalSampler attribute), [1] ExplicitDensityPrior (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors)

F

F (in module jaxns.internals.maps) f (NewtonDiagnostic attribute) f_prop (NewtonDiagnostic attribute) f_quad (NewtonDiagnostic attribute) fast_perfect_live_point_computation_jax() (in module jaxns.internals.tree_structure) final_res_norm (CGDiagnostics attribute) fix_left (ForcedIdentifiability attribute), [1], [2] fix_right (ForcedIdentifiability attribute), [1], [2]

float_type (in module jaxns.internals.mixed_precision) FloatArray (in module jaxns.internals.types) ForcedIdentifiability (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) forward() (AbstractModel method) (Model method), [1], [2] from_signed_value() (LogSpace static method) FV (in module jaxns.internals.maps)

G

g_norm (NewtonDiagnostic attribute) gain_ratio (NewtonDiagnostic attribute) get_grandparent_info() (in module jaxns.internals.logging) get_index() (in module jaxns.internals.maps) get_interp_indices_and_weights() (in module jaxns.internals.interp_utils) get_parameter() (in module jaxns.framework.context) get_sample() (AbstractSampler method) get_sample_from_seed() (BaseAbstractMarkovSampler method) (MultiDimSliceSampler method), [1] (UniDimSliceSampler method), [1] get_seed_point() (BaseAbstractMarkovSampler method) (MultiDimSliceSampler method), [1] (UniDimSliceSampler method), [1] get_state() (in module jaxns.framework.context)

GlobalOptimisation (class in jaxns.experimental) (class in jaxns.experimental.public) GlobalOptimisationResults (class in jaxns.experimental) (class in jaxns.experimental.global_optimisation) GlobalOptimisationState (class in jaxns.experimental) (class in jaxns.experimental.global_optimisation) GlobalOptimisationTerminationCondition (class in jaxns.experimental) (class in jaxns.experimental.global_optimisation) grad_and_hvp() (in module jaxns.experimental.solvers.ad_utils) gradient_guided (NestedSampler attribute), [1] (UniDimSliceSampler attribute), [1] gradient_slice (GlobalOptimisation attribute), [1] (UniDimSliceSampler attribute), [1] gtol (EvidenceMaximisation attribute), [1]

H

H_mean (NestedSamplerResults attribute), [1], [2], [3], [4] high (ForcedIdentifiability attribute), [1], [2] (TruncationWrapper attribute), [1], [2]

hvp_forward_over_reverse() (in module jaxns.experimental.solvers.ad_utils) hvp_linearized() (in module jaxns.experimental.solvers.ad_utils) hvp_reverse_over_forward() (in module jaxns.experimental.solvers.ad_utils) hvp_reverse_over_reverse() (in module jaxns.experimental.solvers.ad_utils)

I

in_trust_region (NewtonDiagnostic attribute) index_dtype (Policy attribute) init_efficiency_threshold (NestedSampler attribute), [1] (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] init_params() (Model method), [1], [2] initialize_params() (in module jaxns.samplers.multi_ellipsoid.em_gmm) int_type (in module jaxns.internals.mixed_precision) IntArray (in module jaxns.internals.types)

InterpolatedArray (class in jaxns.internals.interp_utils) InvalidDistribution InvalidPriorName, [1], [2] is_complex() (in module jaxns.internals.log_semiring) isinstance_namedtuple() (in module jaxns.internals.namedtuple_utils) issubclass_namedtuple() (in module jaxns.internals.namedtuple_utils) items() (ScopedDict method) iteration (NewtonDiagnostic attribute) iterations (CGDiagnostics attribute)

J

jaxify_likelihood() (in module jaxns) (in module jaxns.framework) (in module jaxns.framework.jaxify) jaxns module jaxns.experimental module jaxns.experimental.evidence_maximisation module jaxns.experimental.global_optimisation module jaxns.experimental.public module jaxns.experimental.solvers module jaxns.experimental.solvers.ad_utils module jaxns.experimental.solvers.cg module jaxns.experimental.solvers.gauss_newton_cg module jaxns.experimental.solvers.test_cg module jaxns.framework module jaxns.framework.abc module jaxns.framework.bases module jaxns.framework.context module jaxns.framework.jaxify module jaxns.framework.model module jaxns.framework.ops module jaxns.framework.prior module jaxns.framework.special_priors module jaxns.framework.wrapped_tfp_distribution module jaxns.internals module jaxns.internals.constraint_bijections module jaxns.internals.cumulative_ops module jaxns.internals.interp_utils module jaxns.internals.linalg module jaxns.internals.log_semiring module jaxns.internals.logging module jaxns.internals.maps module jaxns.internals.mixed_precision module jaxns.internals.namedtuple_utils module

jaxns.internals.prefix_sum module jaxns.internals.pytree_utils module jaxns.internals.random module jaxns.internals.shapes module jaxns.internals.shrinkage_statistics module jaxns.internals.stats module jaxns.internals.tree_structure module jaxns.internals.types module jaxns.nested_samplers module jaxns.nested_samplers.abc module jaxns.nested_samplers.common module jaxns.nested_samplers.common.initialisation module jaxns.nested_samplers.common.termination module jaxns.nested_samplers.common.types module jaxns.nested_samplers.common.uniform_sample module jaxns.nested_samplers.sharded module jaxns.nested_samplers.sharded.sharded_static module jaxns.plotting module jaxns.public module jaxns.samplers module jaxns.samplers.abc module jaxns.samplers.bases module jaxns.samplers.multi_ellipsoid module jaxns.samplers.multi_ellipsoid.em_gmm module jaxns.samplers.multi_ellipsoid.multi_ellipsoid_utils module jaxns.samplers.multi_ellipsoidal_samplers module jaxns.samplers.multi_slice_sampler module jaxns.samplers.uni_slice_sampler module jaxns.samplers.uniform_samplers module jaxns.utils module jaxns.warnings module

K

k (GlobalOptimisation attribute), [1] (NestedSampler attribute), [1] key (EphemeralState attribute) (GlobalOptimisationState attribute), [1] (NestedSamplerState attribute), [1], [2], [3], [4]

keys() (ScopedDict method)

L

left_broadcast_multiply() (in module jaxns.internals.interp_utils) LikelihoodInputType (in module jaxns.internals.types) LikelihoodType (in module jaxns.internals.types) linear_to_log_stats() (in module jaxns.internals.stats) live_evidence_frac (TerminationCondition attribute), [1], [2], [3], [4] live_points_collection (EphemeralState attribute) load_pytree() (in module jaxns) (in module jaxns.utils) load_results() (in module jaxns) (in module jaxns.utils) log() (LogSpace method) log_abs_val (LogSpace property) log_dp_mean (NestedSamplerResults attribute), [1], [2], [3], [4] log_efficiency (NestedSamplerResults attribute), [1], [2], [3], [4] log_L (SampleTreeGraph attribute) log_L0 (SeedPoint attribute) log_L_contour (TerminationCondition attribute), [1], [2], [3], [4] log_L_next (EvidenceUpdateVariables attribute)

log_L_progress (GlobalOptimisationResults attribute), [1] log_L_samples (NestedSamplerResults attribute), [1], [2], [3], [4] log_L_solution (GlobalOptimisationResults attribute), [1] log_likelihood_contour (GlobalOptimisationTerminationCondition attribute), [1] log_posterior_density (NestedSamplerResults attribute), [1], [2], [3], [4] log_prob_joint() (AbstractModel method) log_prob_likelihood() (AbstractModel method) log_prob_prior() (AbstractModel method) (Model method), [1], [2] log_X_mean (NestedSamplerResults attribute), [1], [2], [3], [4] log_Z_atol (EvidenceMaximisation attribute), [1] log_Z_ftol (EvidenceMaximisation attribute), [1] log_Z_mean (NestedSamplerResults attribute), [1], [2], [3], [4] log_Z_uncert (NestedSamplerResults attribute), [1], [2], [3], [4] logaddexp() (in module jaxns.internals.log_semiring) logger (in module jaxns.internals.logging) LogSpace (class in jaxns.internals.log_semiring) low (ForcedIdentifiability attribute), [1], [2] (TruncationWrapper attribute), [1], [2]

M

m_step() (EvidenceMaximisation method), [1] (in module jaxns.samplers.multi_ellipsoid.em_gmm) marginalise_dynamic() (in module jaxns) (in module jaxns.utils) marginalise_dynamic_from_U() (in module jaxns) (in module jaxns.utils) marginalise_static() (in module jaxns) (in module jaxns.utils) marginalise_static_from_U() (in module jaxns) (in module jaxns.utils) max() (LogSpace method) max_likelihood_evals (UniformSampler attribute), [1] max_likelihood_evaluations (GlobalOptimisationTerminationCondition attribute), [1] max_num_ellipsoids (MultiEllipsoidalSampler property), [1] max_num_epochs (EvidenceMaximisation attribute), [1] max_num_likelihood_evaluations (TerminationCondition attribute), [1], [2], [3], [4] max_samples (NestedSampler attribute), [1] (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] (TerminationCondition attribute), [1], [2], [3], [4] maximum() (LogSpace method) maximum_a_posteriori_point() (in module jaxns) (in module jaxns.utils) mean() (LogSpace method) measure_dtype (Policy attribute) MeasureType (in module jaxns.internals.types) midpoint_shrink (UniDimSliceSampler attribute), [1] min() (LogSpace method) min_efficiency (GlobalOptimisationTerminationCondition attribute), [1] minimum() (LogSpace method) Model (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.model) model (EvidenceMaximisation attribute), [1] (GlobalOptimisation attribute), [1] (MultiDimSliceSampler attribute), [1] (MultiEllipsoidalSampler attribute), [1] (NestedSampler attribute), [1] (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] (SimpleGlobalOptimisation attribute), [1] (UniDimSliceSampler attribute), [1] (UniformSampler attribute), [1] module jaxns jaxns.experimental jaxns.experimental.evidence_maximisation jaxns.experimental.global_optimisation jaxns.experimental.public jaxns.experimental.solvers jaxns.experimental.solvers.ad_utils jaxns.experimental.solvers.cg jaxns.experimental.solvers.gauss_newton_cg jaxns.experimental.solvers.test_cg jaxns.framework jaxns.framework.abc jaxns.framework.bases jaxns.framework.context jaxns.framework.jaxify jaxns.framework.model jaxns.framework.ops jaxns.framework.prior jaxns.framework.special_priors jaxns.framework.wrapped_tfp_distribution jaxns.internals jaxns.internals.constraint_bijections jaxns.internals.cumulative_ops jaxns.internals.interp_utils jaxns.internals.linalg jaxns.internals.log_semiring jaxns.internals.logging jaxns.internals.maps jaxns.internals.mixed_precision jaxns.internals.namedtuple_utils jaxns.internals.prefix_sum jaxns.internals.pytree_utils jaxns.internals.random jaxns.internals.shapes jaxns.internals.shrinkage_statistics jaxns.internals.stats jaxns.internals.tree_structure jaxns.internals.types jaxns.nested_samplers jaxns.nested_samplers.abc jaxns.nested_samplers.common jaxns.nested_samplers.common.initialisation jaxns.nested_samplers.common.termination jaxns.nested_samplers.common.types jaxns.nested_samplers.common.uniform_sample jaxns.nested_samplers.sharded jaxns.nested_samplers.sharded.sharded_static jaxns.plotting jaxns.public jaxns.samplers jaxns.samplers.abc jaxns.samplers.bases jaxns.samplers.multi_ellipsoid jaxns.samplers.multi_ellipsoid.em_gmm jaxns.samplers.multi_ellipsoid.multi_ellipsoid_utils jaxns.samplers.multi_ellipsoidal_samplers jaxns.samplers.multi_slice_sampler jaxns.samplers.uni_slice_sampler jaxns.samplers.uniform_samplers jaxns.utils jaxns.warnings

mp_policy (in module jaxns.internals.mixed_precision) msqrt() (in module jaxns.internals.linalg) mu (NewtonDiagnostic attribute) MultEllipsoidState (class in jaxns.samplers.multi_ellipsoid.multi_ellipsoid_utils) MultiDimSliceSampler (class in jaxns.samplers) (class in jaxns.samplers.multi_slice_sampler) MultiEllipsoidalSampler (class in jaxns.samplers) (class in jaxns.samplers.multi_ellipsoidal_samplers)

N

n (ForcedIdentifiability attribute), [1], [2] name (Prior attribute), [1], [2] nansum() (LogSpace method) nested_sampler (NestedSampler property), [1] NestedSampler (class in jaxns) (class in jaxns.public) NestedSamplerResults (class in jaxns) (class in jaxns.nested_samplers) (class in jaxns.nested_samplers.common) (class in jaxns.nested_samplers.common.types) (class in jaxns.samplers) NestedSamplerState (class in jaxns) (class in jaxns.nested_samplers) (class in jaxns.nested_samplers.common) (class in jaxns.nested_samplers.common.types) (class in jaxns.samplers) newton_cg_solver() (in module jaxns.experimental.solvers.gauss_newton_cg) NewtonDiagnostic (class in jaxns.experimental.solvers.gauss_newton_cg) next_rng_key() (in module jaxns.framework.context) next_sample_idx (NestedSamplerState attribute), [1], [2], [3], [4] normal_to_lognormal() (in module jaxns.internals.stats) normalise_log_space() (in module jaxns.internals.log_semiring) ns_kwargs (EvidenceMaximisation attribute), [1] num_dynamic_refinement_iterations (ShardedStaticNestedSampler attribute), [1], [2], [3], [4]

num_likelihood_evaluations (GlobalOptimisationResults attribute), [1] (GlobalOptimisationState attribute), [1] num_likelihood_evaluations_per_sample (NestedSamplerResults attribute), [1], [2], [3], [4] num_live_points (EvidenceUpdateVariables attribute) (NestedSampler attribute), [1] (SampleLivePointCounts attribute) (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] num_live_points_per_sample (NestedSamplerResults attribute), [1], [2], [3], [4] num_params (Model property), [1], [2] num_phantom() (AbstractSampler method) (MultiDimSliceSampler method), [1] (MultiEllipsoidalSampler method), [1] (UniDimSliceSampler method), [1] (UniformSampler method), [1] num_phantom_save (MultiDimSliceSampler attribute), [1] (UniDimSliceSampler attribute), [1] num_restrict_dims (MultiDimSliceSampler attribute), [1] num_samples (GlobalOptimisationResults attribute), [1] (GlobalOptimisationState attribute), [1] (NestedSamplerState attribute), [1], [2], [3], [4] num_search_chains (GlobalOptimisation attribute), [1] (SimpleGlobalOptimisation attribute), [1] num_slices (MultiDimSliceSampler attribute), [1] (NestedSampler attribute), [1] (UniDimSliceSampler attribute), [1]

O

ObjectiveRet (in module jaxns.experimental.solvers.gauss_newton_cg)

P

parameter_estimation (NestedSampler attribute), [1] parametrised() (Prior method), [1], [2] parametrised_samples (NestedSamplerResults attribute), [1], [2], [3], [4] params (Model property), [1], [2] (MultEllipsoidState attribute) peak_XL_frac (TerminationCondition attribute), [1], [2], [3], [4] perfect (UniDimSliceSampler attribute), [1] plot_cornerplot() (in module jaxns) (in module jaxns.plotting) plot_diagnostics() (in module jaxns) (in module jaxns.plotting) plot_tree() (in module jaxns.internals.tree_structure) Poisson (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) Policy (class in jaxns.internals.mixed_precision) pop_scope() (ScopedDict method) post_process() (AbstractSampler method) pre_process() (AbstractSampler method) prepad() (in module jaxns.internals.maps)

prepare_func_args() (in module jaxns.internals.maps) prepare_input() (AbstractModel method) (Model method), [1], [2] Prior (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.prior) prior (TruncationWrapper attribute), [1], [2] PriorModelGen (in module jaxns) (in module jaxns.framework) (in module jaxns.framework.bases) PriorModelType (in module jaxns) (in module jaxns.framework) (in module jaxns.framework.bases) PRNGKey (in module jaxns.internals.types) PT (in module jaxns.internals.maps) push_scope() (ScopedDict method) PV (in module jaxns.internals.maps) PyTree (class in jaxns.internals.maps) pytree_unpack() (in module jaxns.internals.maps) pytree_unravel() (in module jaxns.internals.maps)

Q

quick_unit() (in module jaxns.internals.constraint_bijections)

quick_unit_inverse() (in module jaxns.internals.constraint_bijections)

R

random_ortho_matrix() (in module jaxns.internals.random) RandomVariableType (in module jaxns.internals.types) refine_threshold (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] regular_grid (InterpolatedArray attribute) relative_spread (GlobalOptimisationResults attribute), [1] (GlobalOptimisationState attribute), [1]

remove_chunk_dim() (in module jaxns.internals.maps) replace_index() (in module jaxns.internals.maps) resample() (in module jaxns) (in module jaxns.utils) resample_indicies() (in module jaxns.internals.random) rtol (GlobalOptimisationTerminationCondition attribute), [1] (TerminationCondition attribute), [1], [2], [3], [4]

S

s (GlobalOptimisation attribute), [1] (NestedSampler attribute), [1] sample_collection (NestedSamplerState attribute), [1], [2], [3], [4] sample_evidence() (in module jaxns) (in module jaxns.utils) sample_multi_ellipsoid() (in module jaxns.samplers.multi_ellipsoid.multi_ellipsoid_utils) sample_U() (AbstractModel method) (Model method), [1], [2] sample_W() (Model method), [1], [2] SampleLivePointCounts (class in jaxns.internals.tree_structure) sampler (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] (SimpleGlobalOptimisation attribute), [1] SamplerState (in module jaxns.samplers.abc) samples (GlobalOptimisationState attribute), [1] (NestedSamplerResults attribute), [1], [2], [3], [4] samples_indices (SampleLivePointCounts attribute) SampleTreeGraph (class in jaxns.internals.tree_structure) sanity_check() (AbstractModel method) (Model method), [1], [2] save_pytree() (in module jaxns) (in module jaxns.utils) save_results() (in module jaxns) (in module jaxns.utils) scan_associative() (in module jaxns.internals.prefix_sum) scan_associative_cumulative_op() (in module jaxns.internals.cumulative_ops) scope() (in module jaxns.framework.context) scope_prefix (ScopedDict property) ScopedDict (class in jaxns.framework.context) scopes (ScopedDict attribute) SeedPoint (class in jaxns.samplers.bases)

sender_node_idx (SampleTreeGraph attribute) serialise_jax_ndarray() (in module jaxns.internals.namedtuple_utils) serialise_namedtuple() (in module jaxns.internals.namedtuple_utils) serialise_ndarray() (in module jaxns.internals.namedtuple_utils) set_params() (Model method), [1], [2] set_state() (in module jaxns.framework.context) shape (InterpolatedArray property) ShardedStaticNestedSampler (class in jaxns) (class in jaxns.nested_samplers) (class in jaxns.nested_samplers.sharded) (class in jaxns.nested_samplers.sharded.sharded_static) (class in jaxns.samplers) shell_frac (GlobalOptimisation attribute), [1] (SimpleGlobalOptimisation attribute), [1] shell_fraction (NestedSampler attribute), [1] (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] sign (LogSpace property) signed_log (LogSpace property) signed_logaddexp() (in module jaxns.internals.log_semiring) SimpleGlobalOptimisation (class in jaxns.experimental) (class in jaxns.experimental.global_optimisation) simulate_prior_model() (in module jaxns.framework.ops) size (LogSpace property) solution (GlobalOptimisationResults attribute), [1] SPT (in module jaxns.internals.maps) sqrt() (LogSpace method) square() (LogSpace method) squared_norm() (in module jaxns.internals.linalg) sum() (LogSpace method) summary() (in module jaxns) (in module jaxns.utils)

T

T (in module jaxns.internals.maps) (in module jaxns.internals.mixed_precision) (in module jaxns.samplers.bases) termination_cond (EvidenceMaximisation attribute), [1] termination_reason (GlobalOptimisationResults attribute), [1] (NestedSamplerResults attribute), [1], [2], [3], [4] termination_register (EphemeralState attribute) TerminationCondition (class in jaxns) (class in jaxns.nested_samplers) (class in jaxns.nested_samplers.common) (class in jaxns.nested_samplers.common.types) (class in jaxns.samplers) test_cg() (in module jaxns.experimental.solvers.test_cg) to_dict() (ScopedDict method) to_results() (NestedSampler method), [1] total_num_likelihood_evaluations (NestedSamplerResults attribute), [1], [2], [3], [4] total_num_samples (NestedSamplerResults attribute), [1], [2], [3], [4] total_phantom_samples (NestedSamplerResults attribute), [1], [2], [3], [4] train() (EvidenceMaximisation method), [1] transform() (AbstractModel method) (in module jaxns.framework.context) (Model method), [1], [2] transform_parametrised() (AbstractModel method) (Model method), [1], [2]

transform_with_state() (in module jaxns.framework.context) tree (PyTree attribute) tree_add() (in module jaxns.experimental.solvers.ad_utils) tree_device_put() (in module jaxns.internals.maps) tree_div() (in module jaxns.experimental.solvers.ad_utils) (in module jaxns.internals.pytree_utils) tree_dot() (in module jaxns.experimental.solvers.ad_utils) (in module jaxns.internals.pytree_utils) tree_mul() (in module jaxns.experimental.solvers.ad_utils) (in module jaxns.internals.pytree_utils) tree_neg() (in module jaxns.experimental.solvers.ad_utils) tree_norm() (in module jaxns.experimental.solvers.ad_utils) (in module jaxns.internals.pytree_utils) tree_scalar_mul() (in module jaxns.experimental.solvers.ad_utils) tree_sub() (in module jaxns.experimental.solvers.ad_utils) (in module jaxns.internals.pytree_utils) tree_vdot() (in module jaxns.experimental.solvers.ad_utils) tree_vdot_real_part() (in module jaxns.experimental.solvers.ad_utils) trim_results() (NestedSampler static method), [1] TruncationWrapper (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) tuple_prod() (in module jaxns.internals.shapes)

U

U0 (SeedPoint attribute) U_samples (NestedSamplerResults attribute), [1], [2], [3], [4] U_solution (GlobalOptimisationResults attribute), [1] UniDimSliceSampler (class in jaxns.samplers) (class in jaxns.samplers.uni_slice_sampler) UniformSampler (class in jaxns.samplers) (class in jaxns.samplers.uniform_samplers)

UnnormalisedDirichlet (class in jaxns) (class in jaxns.framework) (class in jaxns.framework.special_priors) update_evicence_calculation() (in module jaxns.internals.shrinkage_statistics) UType (in module jaxns.internals.types)

V

V (in module jaxns.internals.cumulative_ops) value (LogSpace property) (Prior property), [1], [2] values (InterpolatedArray attribute) values() (ScopedDict method)

var() (LogSpace method) verbose (EvidenceMaximisation attribute), [1] (GlobalOptimisation attribute), [1] (NestedSampler attribute), [1] (ShardedStaticNestedSampler attribute), [1], [2], [3], [4] (SimpleGlobalOptimisation attribute), [1]

W

wrap_random() (in module jaxns.framework.context)

WrappedTFPDistribution (class in jaxns.framework.wrapped_tfp_distribution)

X

X (in module jaxns.internals.cumulative_ops) (in module jaxns.internals.mixed_precision) (in module jaxns.internals.prefix_sum)

x (InterpolatedArray attribute) X_solution (GlobalOptimisationResults attribute), [1] XType (in module jaxns.internals.types)

Y

Y (in module jaxns.internals.cumulative_ops)