pyABC - distributed, likelihood-free inference
- Release:
0.12.14
- 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
- What is pyABC about?
- Install
- Examples
- Getting started
- Algorithms and features
- Early stopping of model simulations
- Resuming stored ABC runs
- Custom priors
- Adaptive distances
- Informative distances and summary statistics
- Aggregating and weighting diverse data
- Wasserstein distances
- Data plots
- Measurement noise and exact inference
- Optimal acceptance thresholds
- Discrete parameters
- Look-ahead sampling
- External interfaces
- Application examples
- Parallel sampling
- Data store
- Visualization and analysis
- API reference
- pyabc.acceptor
- pyabc.copasi
- pyabc.distance
- Distances
AcceptAllDistanceAdaptiveAggregatedDistanceAdaptivePNormDistanceAggregatedDistanceBinomialKernelDistanceDistanceWithMeasureListFunctionDistanceFunctionKernelIndependentLaplaceKernelIndependentNormalKernelInfoWeightedPNormDistanceMinMaxDistanceNegativeBinomialKernelNoDistanceNormalKernelPCADistancePNormDistancePercentileDistancePoissonKernelRangeEstimatorDistanceSlicedWassersteinDistanceStochasticKernelWassersteinDistanceZScoreDistance
- pyabc.epsilon
- Epsilons
AcceptanceRateSchemeConstantEpsilonDalySchemeEpsilonEssSchemeExpDecayFixedIterSchemeExpDecayFixedRatioSchemeFrielPettittSchemeListEpsilonListTemperatureMedianEpsilonNoEpsilonPolynomialDecayFixedIterSchemeQuantileEpsilonSilkOptimalEpsilonTemperatureTemperatureBaseTemperatureScheme
- pyabc.external
- pyabc.external.r
- pyabc.external.julia
- pyabc.inference
- pyabc.inference_util
- Inference utilities
AnalysisVarscreate_analysis_id()create_prior_pdf()create_simulate_from_prior_function()create_simulate_function()create_transition_pdf()create_weight_function()eps_from_hist()evaluate_preliminary_particle()evaluate_proposal()generate_valid_proposal()only_simulate_data_for_proposal()termination_criteria_fulfilled()
- pyabc.model
- pyabc.parameters
- pyabc.population
- pyabc.populationstrategy
- pyabc.predictor
- pyabc.random_choice
- pyabc.random_variables
- pyabc.sampler
- Parallel sampling
ConcurrentFutureSamplerDaskDistributedSamplerMappingSamplerMulticoreEvalParallelSamplerMulticoreParticleParallelSamplerRedisEvalParallelSamplerRedisEvalParallelSamplerServerStarterRedisStaticSamplerRedisStaticSamplerServerStarterSamplerSingleCoreSamplernr_cores_available()
- pyabc.settings
- pyabc.sge
- pyabc.storage
- pyabc.sumstat
- pyabc.transition
- pyabc.util
- pyabc.visserver
- pyabc.visualization
- Visualization
plot_acceptance_rates_trajectory()plot_acceptance_rates_trajectory_plotly()plot_contour_2d()plot_contour_2d_lowlevel()plot_contour_matrix()plot_contour_matrix_lowlevel()plot_credible_intervals()plot_credible_intervals_for_time()plot_credible_intervals_plotly()plot_data_callback()plot_data_default()plot_distance_weights()plot_effective_sample_sizes()plot_effective_sample_sizes_plotly()plot_eps_walltime()plot_eps_walltime_lowlevel()plot_eps_walltime_lowlevel_plotly()plot_eps_walltime_plotly()plot_epsilons()plot_epsilons_plotly()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_1d_plotly()plot_kde_2d()plot_kde_2d_highlevel()plot_kde_2d_highlevel_plotly()plot_kde_2d_plotly()plot_kde_matrix()plot_kde_matrix_highlevel()plot_kde_matrix_highlevel_plotly()plot_kde_matrix_plotly()plot_lookahead_acceptance_rates()plot_lookahead_evaluations()plot_lookahead_final_acceptance_fractions()plot_model_probabilities()plot_model_probabilities_plotly()plot_sample_numbers()plot_sample_numbers_plotly()plot_sample_numbers_trajectory()plot_sample_numbers_trajectory_plotly()plot_sensitivity_sankey()plot_total_sample_numbers()plot_total_sample_numbers_plotly()plot_total_walltime()plot_total_walltime_plotly()plot_walltime()plot_walltime_lowlevel()plot_walltime_lowlevel_plotly()plot_walltime_plotly()
- pyabc.weighted_statistics
Developer's guide
About
- Release Notes
- 0.12 Series
- 0.12.14 (2023-11-10)
- 0.12.13 (2023-11-08)
- 0.12.12 (2023-08-18)
- 0.12.11 (2023-07-06)
- 0.12.10 (2023-05-09)
- 0.12.9 (2023-03-01)
- 0.12.8 (2022-11-16)
- 0.12.7 (2022-10-30)
- 0.12.6 (2022-08-30)
- 0.12.5 (2022-06-21)
- 0.12.4 (2022-05-05)
- 0.12.3 (2022-04-05)
- 0.12.2 (2022-03-25)
- 0.12.1 (2022-03-02)
- 0.12.0 (2022-02-23)
- 0.11 series
- 0.10 series
- 0.10.16 (2021-05-11)
- 0.10.15 (2021-05-09)
- 0.10.14 (2021-02-21)
- 0.10.13 (2021-02-04)
- 0.10.12 (2021-01-20)
- 0.10.11 (2021-01-02)
- 0.10.10 (2021-01-01)
- 0.10.9 (2020-11-28)
- 0.10.8 (2020-11-27)
- 0.10.7 (2020-08-20)
- 0.10.6 (2020-08-04)
- 0.10.5 (2020-08-01)
- 0.10.4 (2020-06-15)
- 0.10.3 (2020-05-17)
- 0.10.2 (2020-05-09)
- 0.10.1 (2020-03-17)
- 0.10.0 (2020-02-20)
- 0.9 series
- 0.9.26 (2020-01-24)
- 0.9.25 (2020-01-08)
- 0.9.24 (2019-11-19)
- 0.9.23 (2019-11-10)
- 0.9.22 (2019-11-05)
- 0.9.21 (2019-11-05)
- 0.9.20 (2019-10-30)
- 0.9.19 (2019-10-23)
- 0.9.18 (2019-10-20)
- 0.9.17 (2019-10-10)
- 0.9.16 (2019-10-08)
- 0.9.15 (2019-09-15)
- 0.9.14 (2019-08-08)
- 0.9.13 (2019-06-25)
- 0.9.12 (2019-05-02)
- 0.9.11 (2019-04-01)
- 0.9.10 (2019-03-27)
- 0.9.9 (2019-03-25)
- 0.9.8 (2019-02-21)
- 0.9.7 (2019-02-20)
- 0.9.6 (2019-02-01)
- 0.9.5 (2019-01-17)
- 0.9.4 (2018-12-18)
- 0.9.3 (2018-12-01)
- 0.9.2 (2018-09-10)
- 0.9.1 (2018-06-05)
- 0.9.0
- 0.8 series
- 0.7 series
- 0.6 series
- 0.5 series
- 0.4 series
- 0.3 series
- 0.2 series
- 0.1 series
- 0.12 Series
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