pyABC - distributed, likelihood-free inference
- Release:
0.12.9
- Source code:

pyABC is a framework for distributed, likelihood-free inference. That means, if you have a model and some data and want to know the posterior distribution over the model parameters, i.e. you want to know with which probability which parameters explain the observed data, then pyABC might be for you.
All you need is some way to numerically draw samples from the model, given the model parameters. pyABC “inverts” the model for you and tells you which parameters were well matching and which ones not. You do not need to analytically calculate the likelihood function.
pyABC runs efficiently on multi-core machines and distributed cluster setups. It is easy to use and flexibly extensible.
User's guide
About
Developer's guide
API reference
- API reference
- pyABC
- Inference
ABCSMC
- Distances
AcceptAllDistance
AdaptiveAggregatedDistance
AdaptivePNormDistance
AdaptivePNormDistance.__init__()
AdaptivePNormDistance.configure_sampler()
AdaptivePNormDistance.fit_scales()
AdaptivePNormDistance.get_config()
AdaptivePNormDistance.get_weights()
AdaptivePNormDistance.initialize()
AdaptivePNormDistance.is_adaptive()
AdaptivePNormDistance.requires_calibration()
AdaptivePNormDistance.update()
AggregatedDistance
AggregatedDistance.__call__()
AggregatedDistance.__init__()
AggregatedDistance.configure_sampler()
AggregatedDistance.format_dict()
AggregatedDistance.get_config()
AggregatedDistance.get_for_t_or_latest()
AggregatedDistance.initialize()
AggregatedDistance.is_adaptive()
AggregatedDistance.requires_calibration()
AggregatedDistance.update()
BinomialKernel
Distance
DistanceWithMeasureList
FunctionDistance
FunctionKernel
IndependentLaplaceKernel
IndependentNormalKernel
InfoWeightedPNormDistance
InfoWeightedPNormDistance.__init__()
InfoWeightedPNormDistance.calculate_sensis()
InfoWeightedPNormDistance.configure_sampler()
InfoWeightedPNormDistance.fit_info()
InfoWeightedPNormDistance.get_config()
InfoWeightedPNormDistance.get_weights()
InfoWeightedPNormDistance.initialize()
InfoWeightedPNormDistance.is_adaptive()
InfoWeightedPNormDistance.normalize_sample()
InfoWeightedPNormDistance.requires_calibration()
InfoWeightedPNormDistance.update()
MinMaxDistance
NegativeBinomialKernel
NoDistance
NormalKernel
PCADistance
PNormDistance
PercentileDistance
PoissonKernel
RangeEstimatorDistance
SlicedWassersteinDistance
StochasticKernel
WassersteinDistance
ZScoreDistance
- Acceptors
Acceptor
AcceptorResult
FunctionAcceptor
ScaledPDFNorm
StochasticAcceptor
UniformAcceptor
pdf_norm_from_kernel()
pdf_norm_max_found()
- Models
FunctionModel
IntegratedModel
Model
ModelResult
- Epsilons
AcceptanceRateScheme
ConstantEpsilon
DalyScheme
Epsilon
EssScheme
ExpDecayFixedIterScheme
ExpDecayFixedRatioScheme
FrielPettittScheme
ListEpsilon
ListTemperature
MedianEpsilon
NoEpsilon
PolynomialDecayFixedIterScheme
QuantileEpsilon
SilkOptimalEpsilon
Temperature
TemperatureBase
TemperatureScheme
- Predictor
GPKernelHandle
GPPredictor
HiddenLayerHandle
LassoPredictor
LinearPredictor
MLPPredictor
ModelSelectionPredictor
Predictor
SimplePredictor
root_mean_square_error()
root_mean_square_relative_error()
- Summary statistics
GMMSubsetter
IdSubsetter
IdentitySumstat
PredictorSumstat
Subsetter
Sumstat
- Data store
History
History.db
History.stores_sum_stats
History.id
History.__init__()
History.alive_models()
History.all_runs()
History.append_population()
History.db_size
History.done()
History.get_all_populations()
History.get_distribution()
History.get_ground_truth_parameter()
History.get_model_probabilities()
History.get_nr_particles_per_population()
History.get_population()
History.get_population_extended()
History.get_population_strategy()
History.get_weighted_distances()
History.get_weighted_sum_stats()
History.get_weighted_sum_stats_for_model()
History.max_t
History.model_names()
History.n_populations
History.nr_of_models_alive()
History.observed_sum_stat()
History.store_initial_data()
History.store_pre_population()
History.total_nr_simulations
History.update_after_calibration()
create_sqlite_db_id()
load_dict_from_json()
save_dict_to_json()
- Transitions kernels
AggregatedTransition
DiscreteJumpTransition
DiscreteRandomWalkTransition
DiscreteTransition
GridSearchCV
LocalTransition
ModelPerturbationKernel
MultivariateNormalTransition
NotEnoughParticles
PerturbationKernel
Transition
scott_rule_of_thumb()
silverman_rule_of_thumb()
- Population strategies
AdaptivePopulationSize
ConstantPopulationSize
ListPopulationSize
PopulationStrategy
dec_bound_pop_size_from_env()
- Parallel sampling
ConcurrentFutureSampler
DaskDistributedSampler
MappingSampler
MulticoreEvalParallelSampler
MulticoreParticleParallelSampler
RedisEvalParallelSampler
RedisEvalParallelSampler.__init__()
RedisEvalParallelSampler.check_analysis_variables()
RedisEvalParallelSampler.clear_generation_t()
RedisEvalParallelSampler.create_sample()
RedisEvalParallelSampler.generation_t_was_started()
RedisEvalParallelSampler.maybe_start_next_generation()
RedisEvalParallelSampler.start_generation_t()
RedisEvalParallelSamplerServerStarter
RedisStaticSampler
RedisStaticSamplerServerStarter
Sampler
SingleCoreSampler
nr_cores_available()
- Model parameters
Parameter
ParameterStructure
- Particles and Populations
Particle
Population
Population.__init__()
Population.calculate_model_probabilities()
Population.get_accepted_sum_stats()
Population.get_for_keys()
Population.get_model_probabilities()
Population.get_particles_by_model()
Population.get_weighted_distances()
Population.get_weighted_sum_stats()
Population.update_distances()
Sample
SampleFactory
- Random variables
Distribution
DistributionBase
LowerBoundDecorator
RV
RVBase
RVDecorator
- SGE
SGE
sge_available()
nr_cores_available()
NamedPrinter
DefaultContext
ProfilingContext
- External simulators
ExternalDistance
ExternalHandler
ExternalModel
ExternalSumStat
create_sum_stat()
- R interface via rpy2
R
- Julia interface via PyJulia
Julia
- Copasi
BasicoModel
- Visualization
plot_acceptance_rates_trajectory()
plot_credible_intervals()
plot_credible_intervals_for_time()
plot_data_callback()
plot_data_default()
plot_distance_weights()
plot_effective_sample_sizes()
plot_eps_walltime()
plot_eps_walltime_lowlevel()
plot_epsilons()
plot_histogram_1d()
plot_histogram_1d_lowlevel()
plot_histogram_2d()
plot_histogram_2d_lowlevel()
plot_histogram_matrix()
plot_histogram_matrix_lowlevel()
plot_kde_1d()
plot_kde_1d_highlevel()
plot_kde_2d()
plot_kde_2d_highlevel()
plot_kde_matrix()
plot_kde_matrix_highlevel()
plot_lookahead_acceptance_rates()
plot_lookahead_evaluations()
plot_lookahead_final_acceptance_fractions()
plot_model_probabilities()
plot_sample_numbers()
plot_sample_numbers_trajectory()
plot_sensitivity_sankey()
plot_total_sample_numbers()
plot_total_walltime()
plot_walltime()
plot_walltime_lowlevel()
- Weighted statistics
effective_sample_size()
resample()
resample_deterministic()
weight_checked()
weighted_mean()
weighted_median()
weighted_mse()
weighted_quantile()
weighted_rmse()
weighted_std()
weighted_var()