PyMC Example Gallery ==================== .. toctree:: :hidden: object_index/index .. _introductory: Introductory ------------ .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`General Overview` :img-top: https://raw.githubusercontent.com/pymc-devs/brand/main/pymc/pymc_logos/PyMC_square.svg :link: pymc:pymc_overview :link-type: ref :shadow: none .. grid-item-card:: :doc:`Simple Linear Regression` :img-top: ../_thumbnails/core_notebooks/glm_linear.png :link: pymc:glm_linear :link-type: ref :shadow: none .. grid-item-card:: :doc:`introductory/api_quickstart` :img-top: ../_thumbnails/introductory/api_quickstart.png :link: introductory/api_quickstart :link-type: doc :shadow: none .. _fundamentals: Library Fundamentals -------------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`Distribution Dimensionality` :img-top: ../_thumbnails/core_notebooks/dimensionality.png :link: pymc:dimensionality :link-type: ref :shadow: none .. grid-item-card:: :doc:`PyMC and PyTensor` :img-top: ../_thumbnails/core_notebooks/pytensor_pymc.png :link: pymc:pymc_pytensor :link-type: ref :shadow: none .. grid-item-card:: :doc:`fundamentals/data_container` :img-top: ../_thumbnails/fundamentals/data_container.png :link: fundamentals/data_container :link-type: doc :shadow: none .. _howto: How to ------ .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`Prior and Posterior Predictive Checks` :img-top: ../_thumbnails/core_notebooks/model_comparison.png :link: pymc:posterior_predictive :link-type: ref :shadow: none .. grid-item-card:: :doc:`Model Comparison` :img-top: ../_thumbnails/core_notebooks/posterior_predictive.png :link: pymc:model_comparison :link-type: ref :shadow: none .. grid-item-card:: :doc:`howto/LKJ` :img-top: ../_thumbnails/howto/LKJ.png :link: howto/LKJ :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/Missing_Data_Imputation` :img-top: ../_thumbnails/howto/Missing_Data_Imputation.png :link: howto/Missing_Data_Imputation :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/blackbox_external_likelihood_numpy` :img-top: ../_thumbnails/howto/blackbox_external_likelihood_numpy.png :link: howto/blackbox_external_likelihood_numpy :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/copula-estimation` :img-top: ../_thumbnails/howto/copula-estimation.png :link: howto/copula-estimation :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/howto_debugging` :img-top: ../_thumbnails/howto/howto_debugging.png :link: howto/howto_debugging :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/marginalizing-models` :img-top: ../_thumbnails/howto/marginalizing-models.png :link: howto/marginalizing-models :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/model_builder` :img-top: ../_thumbnails/howto/model_builder.png :link: howto/model_builder :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/profiling` :img-top: ../_thumbnails/howto/profiling.png :link: howto/profiling :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/spline` :img-top: ../_thumbnails/howto/spline.png :link: howto/spline :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/updating_priors` :img-top: ../_thumbnails/howto/updating_priors.png :link: howto/updating_priors :link-type: doc :shadow: none .. grid-item-card:: :doc:`howto/wrapping_jax_function` :img-top: ../_thumbnails/howto/wrapping_jax_function.png :link: howto/wrapping_jax_function :link-type: doc :shadow: none .. _generalized_linear_models: Generalized Linear Models ------------------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`generalized_linear_models/GLM-binomial-regression` :img-top: ../_thumbnails/generalized_linear_models/GLM-binomial-regression.png :link: generalized_linear_models/GLM-binomial-regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-discrete-choice_models` :img-top: ../_thumbnails/generalized_linear_models/GLM-discrete-choice_models.png :link: generalized_linear_models/GLM-discrete-choice_models :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-hierarchical-binomial-model` :img-top: ../_thumbnails/generalized_linear_models/GLM-hierarchical-binomial-model.png :link: generalized_linear_models/GLM-hierarchical-binomial-model :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-missing-values-in-covariates` :img-top: ../_thumbnails/generalized_linear_models/GLM-missing-values-in-covariates.png :link: generalized_linear_models/GLM-missing-values-in-covariates :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-model-selection` :img-top: ../_thumbnails/generalized_linear_models/GLM-model-selection.png :link: generalized_linear_models/GLM-model-selection :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-negative-binomial-regression` :img-top: ../_thumbnails/generalized_linear_models/GLM-negative-binomial-regression.png :link: generalized_linear_models/GLM-negative-binomial-regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-ordinal-features` :img-top: ../_thumbnails/generalized_linear_models/GLM-ordinal-features.png :link: generalized_linear_models/GLM-ordinal-features :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-ordinal-regression` :img-top: ../_thumbnails/generalized_linear_models/GLM-ordinal-regression.png :link: generalized_linear_models/GLM-ordinal-regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-out-of-sample-predictions` :img-top: ../_thumbnails/generalized_linear_models/GLM-out-of-sample-predictions.png :link: generalized_linear_models/GLM-out-of-sample-predictions :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-poisson-regression` :img-top: ../_thumbnails/generalized_linear_models/GLM-poisson-regression.png :link: generalized_linear_models/GLM-poisson-regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-robust-with-outlier-detection` :img-top: ../_thumbnails/generalized_linear_models/GLM-robust-with-outlier-detection.png :link: generalized_linear_models/GLM-robust-with-outlier-detection :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-robust` :img-top: ../_thumbnails/generalized_linear_models/GLM-robust.png :link: generalized_linear_models/GLM-robust :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-rolling-regression` :img-top: ../_thumbnails/generalized_linear_models/GLM-rolling-regression.png :link: generalized_linear_models/GLM-rolling-regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/GLM-truncated-censored-regression` :img-top: ../_thumbnails/generalized_linear_models/GLM-truncated-censored-regression.png :link: generalized_linear_models/GLM-truncated-censored-regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`generalized_linear_models/multilevel_modeling` :img-top: ../_thumbnails/generalized_linear_models/multilevel_modeling.png :link: generalized_linear_models/multilevel_modeling :link-type: doc :shadow: none .. _case_studies: Case Studies ------------ .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`case_studies/BEST` :img-top: ../_thumbnails/case_studies/BEST.png :link: case_studies/BEST :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/CFA_SEM` :img-top: ../_thumbnails/case_studies/CFA_SEM.png :link: case_studies/CFA_SEM :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/GEV` :img-top: ../_thumbnails/case_studies/GEV.png :link: case_studies/GEV :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/binning` :img-top: ../_thumbnails/case_studies/binning.png :link: case_studies/binning :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/factor_analysis` :img-top: ../_thumbnails/case_studies/factor_analysis.png :link: case_studies/factor_analysis :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/hierarchical_partial_pooling` :img-top: ../_thumbnails/case_studies/hierarchical_partial_pooling.png :link: case_studies/hierarchical_partial_pooling :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/item_response_nba` :img-top: ../_thumbnails/case_studies/item_response_nba.png :link: case_studies/item_response_nba :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/probabilistic_matrix_factorization` :img-top: ../_thumbnails/case_studies/probabilistic_matrix_factorization.png :link: case_studies/probabilistic_matrix_factorization :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/putting_workflow` :img-top: ../_thumbnails/case_studies/putting_workflow.png :link: case_studies/putting_workflow :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/reinforcement_learning` :img-top: ../_thumbnails/case_studies/reinforcement_learning.png :link: case_studies/reinforcement_learning :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/reliability_and_calibrated_prediction` :img-top: ../_thumbnails/case_studies/reliability_and_calibrated_prediction.png :link: case_studies/reliability_and_calibrated_prediction :link-type: doc :shadow: none .. grid-item-card:: :doc:`case_studies/rugby_analytics` :img-top: ../_thumbnails/case_studies/rugby_analytics.png :link: case_studies/rugby_analytics :link-type: doc :shadow: none .. _causal_inference: Causal Inference ---------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`causal_inference/GLM-simpsons-paradox` :img-top: ../_thumbnails/causal_inference/GLM-simpsons-paradox.png :link: causal_inference/GLM-simpsons-paradox :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/bayesian_ab_testing_introduction` :img-top: ../_thumbnails/causal_inference/bayesian_ab_testing_introduction.png :link: causal_inference/bayesian_ab_testing_introduction :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/bayesian_nonparametric_causal` :img-top: ../_thumbnails/causal_inference/bayesian_nonparametric_causal.png :link: causal_inference/bayesian_nonparametric_causal :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/difference_in_differences` :img-top: ../_thumbnails/causal_inference/difference_in_differences.png :link: causal_inference/difference_in_differences :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/excess_deaths` :img-top: ../_thumbnails/causal_inference/excess_deaths.png :link: causal_inference/excess_deaths :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/interrupted_time_series` :img-top: ../_thumbnails/causal_inference/interrupted_time_series.png :link: causal_inference/interrupted_time_series :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/interventional_distribution` :img-top: ../_thumbnails/causal_inference/interventional_distribution.png :link: causal_inference/interventional_distribution :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/mediation_analysis` :img-top: ../_thumbnails/causal_inference/mediation_analysis.png :link: causal_inference/mediation_analysis :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/moderation_analysis` :img-top: ../_thumbnails/causal_inference/moderation_analysis.png :link: causal_inference/moderation_analysis :link-type: doc :shadow: none .. grid-item-card:: :doc:`causal_inference/regression_discontinuity` :img-top: ../_thumbnails/causal_inference/regression_discontinuity.png :link: causal_inference/regression_discontinuity :link-type: doc :shadow: none .. _gaussian_processes: Gaussian Processes ------------------ .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`gaussian_processes/GP-Births` :img-top: ../_thumbnails/gaussian_processes/GP-Births.png :link: gaussian_processes/GP-Births :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-Circular` :img-top: ../_thumbnails/gaussian_processes/GP-Circular.png :link: gaussian_processes/GP-Circular :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-Heteroskedastic` :img-top: ../_thumbnails/gaussian_processes/GP-Heteroskedastic.png :link: gaussian_processes/GP-Heteroskedastic :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-Kron` :img-top: ../_thumbnails/gaussian_processes/GP-Kron.png :link: gaussian_processes/GP-Kron :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-Latent` :img-top: ../_thumbnails/gaussian_processes/GP-Latent.png :link: gaussian_processes/GP-Latent :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-Marginal` :img-top: ../_thumbnails/gaussian_processes/GP-Marginal.png :link: gaussian_processes/GP-Marginal :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-MaunaLoa` :img-top: ../_thumbnails/gaussian_processes/GP-MaunaLoa.png :link: gaussian_processes/GP-MaunaLoa :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-MaunaLoa2` :img-top: ../_thumbnails/gaussian_processes/GP-MaunaLoa2.png :link: gaussian_processes/GP-MaunaLoa2 :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-MeansAndCovs` :img-top: ../_thumbnails/gaussian_processes/GP-MeansAndCovs.png :link: gaussian_processes/GP-MeansAndCovs :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-SparseApprox` :img-top: ../_thumbnails/gaussian_processes/GP-SparseApprox.png :link: gaussian_processes/GP-SparseApprox :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-TProcess` :img-top: ../_thumbnails/gaussian_processes/GP-TProcess.png :link: gaussian_processes/GP-TProcess :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/GP-smoothing` :img-top: ../_thumbnails/gaussian_processes/GP-smoothing.png :link: gaussian_processes/GP-smoothing :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/HSGP-Advanced` :img-top: ../_thumbnails/gaussian_processes/HSGP-Advanced.png :link: gaussian_processes/HSGP-Advanced :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/HSGP-Basic` :img-top: ../_thumbnails/gaussian_processes/HSGP-Basic.png :link: gaussian_processes/HSGP-Basic :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/MOGP-Coregion-Hadamard` :img-top: ../_thumbnails/gaussian_processes/MOGP-Coregion-Hadamard.png :link: gaussian_processes/MOGP-Coregion-Hadamard :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/gaussian_process` :img-top: ../_thumbnails/gaussian_processes/gaussian_process.png :link: gaussian_processes/gaussian_process :link-type: doc :shadow: none .. grid-item-card:: :doc:`gaussian_processes/log-gaussian-cox-process` :img-top: ../_thumbnails/gaussian_processes/log-gaussian-cox-process.png :link: gaussian_processes/log-gaussian-cox-process :link-type: doc :shadow: none .. _time_series: Time Series ----------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`time_series/AR` :img-top: ../_thumbnails/time_series/AR.png :link: time_series/AR :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/Air_passengers-Prophet_with_Bayesian_workflow` :img-top: ../_thumbnails/time_series/Air_passengers-Prophet_with_Bayesian_workflow.png :link: time_series/Air_passengers-Prophet_with_Bayesian_workflow :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/Euler-Maruyama_and_SDEs` :img-top: ../_thumbnails/time_series/Euler-Maruyama_and_SDEs.png :link: time_series/Euler-Maruyama_and_SDEs :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/Forecasting_with_structural_timeseries` :img-top: ../_thumbnails/time_series/Forecasting_with_structural_timeseries.png :link: time_series/Forecasting_with_structural_timeseries :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/MvGaussianRandomWalk_demo` :img-top: ../_thumbnails/time_series/MvGaussianRandomWalk_demo.png :link: time_series/MvGaussianRandomWalk_demo :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/Time_Series_Generative_Graph` :img-top: ../_thumbnails/time_series/Time_Series_Generative_Graph.png :link: time_series/Time_Series_Generative_Graph :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/bayesian_var_model` :img-top: ../_thumbnails/time_series/bayesian_var_model.png :link: time_series/bayesian_var_model :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/longitudinal_models` :img-top: ../_thumbnails/time_series/longitudinal_models.png :link: time_series/longitudinal_models :link-type: doc :shadow: none .. grid-item-card:: :doc:`time_series/stochastic_volatility` :img-top: ../_thumbnails/time_series/stochastic_volatility.png :link: time_series/stochastic_volatility :link-type: doc :shadow: none .. _spatial: Spatial Analysis ---------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`spatial/conditional_autoregressive_priors` :img-top: ../_thumbnails/spatial/conditional_autoregressive_priors.png :link: spatial/conditional_autoregressive_priors :link-type: doc :shadow: none .. grid-item-card:: :doc:`spatial/malaria_prevalence` :img-top: ../_thumbnails/spatial/malaria_prevalence.png :link: spatial/malaria_prevalence :link-type: doc :shadow: none .. grid-item-card:: :doc:`spatial/nyc_bym` :img-top: ../_thumbnails/spatial/nyc_bym.png :link: spatial/nyc_bym :link-type: doc :shadow: none .. _diagnostics_and_criticism: Diagnostics and Model Criticism ------------------------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`diagnostics_and_criticism/Bayes_factor` :img-top: ../_thumbnails/diagnostics_and_criticism/Bayes_factor.png :link: diagnostics_and_criticism/Bayes_factor :link-type: doc :shadow: none .. grid-item-card:: :doc:`diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences` :img-top: ../_thumbnails/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.png :link: diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences :link-type: doc :shadow: none .. grid-item-card:: :doc:`diagnostics_and_criticism/model_averaging` :img-top: ../_thumbnails/diagnostics_and_criticism/model_averaging.png :link: diagnostics_and_criticism/model_averaging :link-type: doc :shadow: none .. grid-item-card:: :doc:`diagnostics_and_criticism/sampler-stats` :img-top: ../_thumbnails/diagnostics_and_criticism/sampler-stats.png :link: diagnostics_and_criticism/sampler-stats :link-type: doc :shadow: none .. _bart: Bayesian Additive Regression Trees ---------------------------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`bart/bart_categorical_hawks` :img-top: ../_thumbnails/bart/bart_categorical_hawks.png :link: bart/bart_categorical_hawks :link-type: doc :shadow: none .. grid-item-card:: :doc:`bart/bart_heteroscedasticity` :img-top: ../_thumbnails/bart/bart_heteroscedasticity.png :link: bart/bart_heteroscedasticity :link-type: doc :shadow: none .. grid-item-card:: :doc:`bart/bart_introduction` :img-top: ../_thumbnails/bart/bart_introduction.png :link: bart/bart_introduction :link-type: doc :shadow: none .. grid-item-card:: :doc:`bart/bart_quantile_regression` :img-top: ../_thumbnails/bart/bart_quantile_regression.png :link: bart/bart_quantile_regression :link-type: doc :shadow: none .. _mixture_models: Mixture Models -------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`mixture_models/dependent_density_regression` :img-top: ../_thumbnails/mixture_models/dependent_density_regression.png :link: mixture_models/dependent_density_regression :link-type: doc :shadow: none .. grid-item-card:: :doc:`mixture_models/dirichlet_mixture_of_multinomials` :img-top: ../_thumbnails/mixture_models/dirichlet_mixture_of_multinomials.png :link: mixture_models/dirichlet_mixture_of_multinomials :link-type: doc :shadow: none .. grid-item-card:: :doc:`mixture_models/dp_mix` :img-top: ../_thumbnails/mixture_models/dp_mix.png :link: mixture_models/dp_mix :link-type: doc :shadow: none .. grid-item-card:: :doc:`mixture_models/gaussian_mixture_model` :img-top: ../_thumbnails/mixture_models/gaussian_mixture_model.png :link: mixture_models/gaussian_mixture_model :link-type: doc :shadow: none .. grid-item-card:: :doc:`mixture_models/marginalized_gaussian_mixture_model` :img-top: ../_thumbnails/mixture_models/marginalized_gaussian_mixture_model.png :link: mixture_models/marginalized_gaussian_mixture_model :link-type: doc :shadow: none .. _survival_analysis: Survival Analysis ----------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`survival_analysis/bayes_param_survival` :img-top: ../_thumbnails/survival_analysis/bayes_param_survival.png :link: survival_analysis/bayes_param_survival :link-type: doc :shadow: none .. grid-item-card:: :doc:`survival_analysis/censored_data` :img-top: ../_thumbnails/survival_analysis/censored_data.png :link: survival_analysis/censored_data :link-type: doc :shadow: none .. grid-item-card:: :doc:`survival_analysis/frailty_models` :img-top: ../_thumbnails/survival_analysis/frailty_models.png :link: survival_analysis/frailty_models :link-type: doc :shadow: none .. grid-item-card:: :doc:`survival_analysis/survival_analysis` :img-top: ../_thumbnails/survival_analysis/survival_analysis.png :link: survival_analysis/survival_analysis :link-type: doc :shadow: none .. grid-item-card:: :doc:`survival_analysis/weibull_aft` :img-top: ../_thumbnails/survival_analysis/weibull_aft.png :link: survival_analysis/weibull_aft :link-type: doc :shadow: none .. _ode_models: ODE models ---------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`ode_models/ODE_API_introduction` :img-top: ../_thumbnails/ode_models/ODE_API_introduction.png :link: ode_models/ODE_API_introduction :link-type: doc :shadow: none .. grid-item-card:: :doc:`ode_models/ODE_API_shapes_and_benchmarking` :img-top: ../_thumbnails/ode_models/ODE_API_shapes_and_benchmarking.png :link: ode_models/ODE_API_shapes_and_benchmarking :link-type: doc :shadow: none .. grid-item-card:: :doc:`ode_models/ODE_Lotka_Volterra_multiple_ways` :img-top: ../_thumbnails/ode_models/ODE_Lotka_Volterra_multiple_ways.png :link: ode_models/ODE_Lotka_Volterra_multiple_ways :link-type: doc :shadow: none .. grid-item-card:: :doc:`ode_models/ODE_with_manual_gradients` :img-top: ../_thumbnails/ode_models/ODE_with_manual_gradients.png :link: ode_models/ODE_with_manual_gradients :link-type: doc :shadow: none .. _samplers: MCMC ---- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`samplers/DEMetropolisZ_EfficiencyComparison` :img-top: ../_thumbnails/samplers/DEMetropolisZ_EfficiencyComparison.png :link: samplers/DEMetropolisZ_EfficiencyComparison :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/DEMetropolisZ_tune_drop_fraction` :img-top: ../_thumbnails/samplers/DEMetropolisZ_tune_drop_fraction.png :link: samplers/DEMetropolisZ_tune_drop_fraction :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/SMC-ABC_Lotka-Volterra_example` :img-top: ../_thumbnails/samplers/SMC-ABC_Lotka-Volterra_example.png :link: samplers/SMC-ABC_Lotka-Volterra_example :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/SMC2_gaussians` :img-top: ../_thumbnails/samplers/SMC2_gaussians.png :link: samplers/SMC2_gaussians :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/fast_sampling_with_jax_and_numba` :img-top: ../_thumbnails/samplers/fast_sampling_with_jax_and_numba.png :link: samplers/fast_sampling_with_jax_and_numba :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/lasso_block_update` :img-top: ../_thumbnails/samplers/lasso_block_update.png :link: samplers/lasso_block_update :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/sampling_compound_step` :img-top: ../_thumbnails/samplers/sampling_compound_step.png :link: samplers/sampling_compound_step :link-type: doc :shadow: none .. grid-item-card:: :doc:`samplers/sampling_conjugate_step` :img-top: ../_thumbnails/samplers/sampling_conjugate_step.png :link: samplers/sampling_conjugate_step :link-type: doc :shadow: none .. _variational_inference: Variational Inference --------------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`variational_inference/GLM-hierarchical-advi-minibatch` :img-top: ../_thumbnails/variational_inference/GLM-hierarchical-advi-minibatch.png :link: variational_inference/GLM-hierarchical-advi-minibatch :link-type: doc :shadow: none .. grid-item-card:: :doc:`variational_inference/bayesian_neural_network_advi` :img-top: ../_thumbnails/variational_inference/bayesian_neural_network_advi.png :link: variational_inference/bayesian_neural_network_advi :link-type: doc :shadow: none .. grid-item-card:: :doc:`variational_inference/empirical-approx-overview` :img-top: ../_thumbnails/variational_inference/empirical-approx-overview.png :link: variational_inference/empirical-approx-overview :link-type: doc :shadow: none .. grid-item-card:: :doc:`variational_inference/pathfinder` :img-top: ../_thumbnails/variational_inference/pathfinder.png :link: variational_inference/pathfinder :link-type: doc :shadow: none .. grid-item-card:: :doc:`variational_inference/variational_api_quickstart` :img-top: ../_thumbnails/variational_inference/variational_api_quickstart.png :link: variational_inference/variational_api_quickstart :link-type: doc :shadow: none .. _statistical_rethinking_lectures: Statistical Rethinking Lectures ------------------------------- .. grid:: 1 2 3 3 :gutter: 4 .. grid-item-card:: :doc:`statistical_rethinking_lectures/02-The_Garden_of_Forking_Data` :img-top: ../_thumbnails/statistical_rethinking_lectures/02-The_Garden_of_Forking_Data.png :link: statistical_rethinking_lectures/02-The_Garden_of_Forking_Data :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/03-Geocentric_Models` :img-top: ../_thumbnails/statistical_rethinking_lectures/03-Geocentric_Models.png :link: statistical_rethinking_lectures/03-Geocentric_Models :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/04-Categories_&_Curves` :img-top: ../_thumbnails/statistical_rethinking_lectures/04-Categories_&_Curves.png :link: statistical_rethinking_lectures/04-Categories_&_Curves :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/05-Elemental_Confounds` :img-top: ../_thumbnails/statistical_rethinking_lectures/05-Elemental_Confounds.png :link: statistical_rethinking_lectures/05-Elemental_Confounds :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/06-Good_&_Bad_Controls` :img-top: ../_thumbnails/statistical_rethinking_lectures/06-Good_&_Bad_Controls.png :link: statistical_rethinking_lectures/06-Good_&_Bad_Controls :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/07-Fitting_Over_&_Under` :img-top: ../_thumbnails/statistical_rethinking_lectures/07-Fitting_Over_&_Under.png :link: statistical_rethinking_lectures/07-Fitting_Over_&_Under :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/08-Markov_Chain_Monte_Carlo` :img-top: ../_thumbnails/statistical_rethinking_lectures/08-Markov_Chain_Monte_Carlo.png :link: statistical_rethinking_lectures/08-Markov_Chain_Monte_Carlo :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/09-Modeling_Events` :img-top: ../_thumbnails/statistical_rethinking_lectures/09-Modeling_Events.png :link: statistical_rethinking_lectures/09-Modeling_Events :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/10-Counts_&_Hidden_Confounds` :img-top: ../_thumbnails/statistical_rethinking_lectures/10-Counts_&_Hidden_Confounds.png :link: statistical_rethinking_lectures/10-Counts_&_Hidden_Confounds :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/11-Ordered_Categories` :img-top: ../_thumbnails/statistical_rethinking_lectures/11-Ordered_Categories.png :link: statistical_rethinking_lectures/11-Ordered_Categories :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/12-Multilevel_Models` :img-top: ../_thumbnails/statistical_rethinking_lectures/12-Multilevel_Models.png :link: statistical_rethinking_lectures/12-Multilevel_Models :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/13-Multilevel_Adventures` :img-top: ../_thumbnails/statistical_rethinking_lectures/13-Multilevel_Adventures.png :link: statistical_rethinking_lectures/13-Multilevel_Adventures :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/14-Correlated_Features` :img-top: ../_thumbnails/statistical_rethinking_lectures/14-Correlated_Features.png :link: statistical_rethinking_lectures/14-Correlated_Features :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/15-Social_Networks` :img-top: ../_thumbnails/statistical_rethinking_lectures/15-Social_Networks.png :link: statistical_rethinking_lectures/15-Social_Networks :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/16-Gaussian_Processes` :img-top: ../_thumbnails/statistical_rethinking_lectures/16-Gaussian_Processes.png :link: statistical_rethinking_lectures/16-Gaussian_Processes :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/17-Measurement_and_Misclassification` :img-top: ../_thumbnails/statistical_rethinking_lectures/17-Measurement_and_Misclassification.png :link: statistical_rethinking_lectures/17-Measurement_and_Misclassification :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/18-Missing_Data` :img-top: ../_thumbnails/statistical_rethinking_lectures/18-Missing_Data.png :link: statistical_rethinking_lectures/18-Missing_Data :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/19-Generalized_Linear_Madness` :img-top: ../_thumbnails/statistical_rethinking_lectures/19-Generalized_Linear_Madness.png :link: statistical_rethinking_lectures/19-Generalized_Linear_Madness :link-type: doc :shadow: none .. grid-item-card:: :doc:`statistical_rethinking_lectures/20-Horoscopes` :img-top: ../_thumbnails/statistical_rethinking_lectures/20-Horoscopes.png :link: statistical_rethinking_lectures/20-Horoscopes :link-type: doc :shadow: none