pymc-devs / pymc-devs/pymc-examples
Updates examples for new major releases of pymc/pytensor/arviz
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- Python
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Description
Arviz v1 is going to break everything for sure, but there might be small details that will stop working due to pymc v6/pytensor v3. This issue is a tracker for checking that everything works post-release.
Introductory
- pymc_overview
- glm_linear
- api_quickstart
Library Fundamentals
- dimensionality
- pymc_pytensor
- data_container
How To
- posterior_predictive
- model_comparison
- lkj
- missing_data_imputation
- blackbox_external_likelihood_numpy
- copula_estimation
- debugging
- hypothesis_testing
- marginalizing_models
- model_builder
- profiling
- spline
- updating_priors
- wrapping_jax_function
Generalized Linear Models
- glm_binomial_regression
- glm_discrete_choice_models
- glm_hierarchical_binomial_model
- glm_missing_values_in_covariates
- glm_model_selection
- glm_negative_binomial_regression
- glm_ordinal_features
- glm_ordinal_regression
- glm_out_of_sample_predictions
- glm_poisson_regression
- glm_robust_with_outlier_detection
- glm_robust
- glm_rolling_regression
- glm_truncated_censored_regression
- multilevel_modeling
Case Studies
- best
- cfa_sem
- gev
- bayesian_sem_workflow
- bayesian_workflow
- binning
- factor_analysis
- hierarchical_partial_pooling
- item_response_nba
- occupancy
- probabilistic_matrix_factorization
- putting_workflow
- reinforcement_learning
- reliability_and_calibrated_prediction
- rugby_analytics
- ssm_hurricane_tracking
Causal Inference
- glm_simpsons_paradox
- bayesian_ab_testing_introduction
- bayesian_nonparametric_causal
- interventional_what_if_do_operator
- difference_in_differences
- excess_deaths
- interrupted_time_series
- interventional_distribution
- mediation_analysis
- moderation_analysis
- regression_discontinuity
Gaussian Processes
- gp_births
- gp_circular
- gp_heteroskedastic
- gp_kron
- gp_latent
- gp_marginal
- gp_maunaloa
- gp_maunaloa2
- gp_means_and_covs
- gp_sparse_approx
- gp_tprocess
- gp_smoothing
- hsgp_advanced
- hsgp_basic
- mogp_coregion_hadamard
- gaussian_process
- log_gaussian_cox_process
Time Series
- ar
- air_passengers_prophet_bayesian_workflow
- euler_maruyama_sdes
- forecasting_structural_timeseries
- mv_gaussian_random_walk_demo
- time_series_generative_graph
- bayesian_var_model
- longitudinal_models
- stochastic_volatility
Spatial Analysis
- conditional_autoregressive_priors
- malaria_prevalence
- nyc_bym
Diagnostics and Model Criticism
- bayes_factor
- diagnosing_biased_inference_with_divergences
- model_averaging
- sampler_stats
Bayesian Additive Regression Trees
- bart_categorical_hawks
- bart_heteroscedasticity
- bart_introduction
- bart_quantile_regression
Mixture Models
- dependent_density_regression
- dirichlet_mixture_of_multinomials
- dp_mix
- gaussian_mixture_model
- marginalized_gaussian_mixture_model
Survival Analysis
- bayes_param_survival
- censored_data
- frailty_models
- survival_analysis
- weibull_aft
ODE Models
- ode_api_introduction
- ode_api_shapes_and_benchmarking
- ode_lotka_volterra_multiple_ways
- ode_with_manual_gradients
MCMC
- demetropolisz_efficiency_comparison
- demetropolisz_tune_drop_fraction
- smc_abc_lotka_volterra_example
- smc2_gaussians
- fast_sampling_jax_numba
- lasso_block_update
- sampling_compound_step
- sampling_conjugate_step
Variational Inference
- glm_hierarchical_advi_minibatch
- bayesian_neural_network_advi
- empirical_approx_overview
- pathfinder
- variational_api_quickstart
Statistical Rethinking Lectures
- sr_02_garden_of_forking_data
- sr_03_geocentric_models
- sr_04_categories_curves
- sr_05_elemental_confounds
- sr_06_good_bad_controls
- sr_07_fitting_over_under
- sr_08_mcmc
- sr_09_modeling_events
- sr_10_counts_hidden_confounds
- sr_11_ordered_categories
- sr_12_multilevel_models
- sr_13_multilevel_adventures
- sr_14_correlated_features
- sr_15_social_networks
- sr_16_gaussian_processes
- sr_17_measurement_misclassification
- sr_18_missing_data
- sr_19_glm_madness
- sr_20_horoscopes
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Use the unchecked notebook names in the issue as the work list, starting with one introductory or fundamentals example. Run that notebook against the new pymc, pytensor, and arviz releases and compare its behavior with the expected example output. Done means the selected notebook works post-release and its checklist item can be marked complete.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100