Add redirects from old v3 notebooks

docs/source/conf.py

@@ -158,8 +158,115 @@
 }
 myst_heading_anchors = None
 
+v3_example_tutorials = [
+    "case_studies/BEST",
+    "case_studies/LKJ",
+    "case_studies/stochastic_volatility",
+    "case_studies/rugby_analytics",
+    "case_studies/multilevel_modeling",
+    "case_studies/putting_workflow",
+    "case_studies/moderation_analysis",
+    "case_studies/mediation_analysis",
+    "case_studies/bayesian_ab_testing",
+    "case_studies/item_response_nba",
+    "diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences",
+    "diagnostics_and_criticism/Bayes_factor",
+    "generalized_linear_models/GLM-logistic",
+    "generalized_linear_models/GLM-binomial-regression",
+    "generalized_linear_models/GLM-hierarchical-binominal-model",
+    "generalized_linear_models/GLM-hierarchical",
+    "case_studies/hierarchical_partial_pooling",
+    "generalized_linear_models/GLM-model-selection",
+    "generalized_linear_models/GLM-negative-binomial-regression",
+    "generalized_linear_models/GLM-out-of-sample-predictions",
+    "generalized_linear_models/GLM-poisson-regression",
+    "generalized_linear_models/GLM-robust-with-outlier-detection",
+    "generalized_linear_models/GLM-robust",
+    "generalized_linear_models/GLM-rolling-regression",
+    "generalized_linear_models/GLM-truncated-censored-regression",
+    "generalized_linear_models/GLM-simpsons-paradox",
+    "gaussian_processes/GP-Kron",
+    "gaussian_processes/GP-Latent",
+    "gaussian_processes/GP-Marginal",
+    "gaussian_processes/GP-MaunaLoa",
+    "gaussian_processes/GP-MaunaLoa2",
+    "gaussian_processes/GP-MeansAndCovs",
+    "gaussian_processes/GP-SparseApprox",
+    "gaussian_processes/GP-TProcess",
+    "gaussian_processes/GP-smoothing",
+    "gaussian_processes/GP-Heteroskedastic",
+    "gaussian_processes/gaussian_process",
+    "case_studies/conditional-autoregressive-model",
+    "case_studies/log-gaussian-cox-process",
+    "gaussian_processes/GP-Circular",
+    "mixture_models/dependent_density_regression",
+    "mixture_models/dp_mix",
+    "variational_inference/gaussian-mixture-model-advi",
+    "mixture_models/gaussian_mixture_model",
+    "mixture_models/marginalized_gaussian_mixture_model",
+    "mixture_models/dirichlet_mixture_of_multinomials",
+    "samplers/SMC2_gaussians",
+    "samplers/SMC-ABC_Lotka-Volterra_example",
+    "survival_analysis/bayes_param_survival_pymc3",
+    "survival_analysis/censored_data",
+    "survival_analysis/survival_analysis",
+    "survival_analysis/weibull_aft",
+    "survival_analysis/cox_model",
+    "time_series/MvGaussianRandomWalk_demo",
+    "time_series/AR",
+    "time_series/Euler-Maruyama_and_SDEs",
+    "time_series/Air_passengers-Prophet_with_Bayesian_workflow",
+    "variational_inference/bayesian_neural_network_advi",
+    "variational_inference/convolutional_vae_keras_advi",
+    "variational_inference/empirical-approx-overview",
+    "variational_inference/lda-advi-aevb",
+    "variational_inference/normalizing_flows_overview",
+    "variational_inference/gaussian-mixture-model-advi",
+    "variational_inference/GLM-hierarchical-advi-minibatch",
+    "ode_models/ODE_with_manual_gradients",
+    "ode_models/ODE_API_introduction",
+    "case_studies/probabilistic_matrix_factorization",
+    "pymc3_howto/sampling_conjugate_step",
+    "samplers/MLDA_introduction",
+    "samplers/MLDA_simple_linear_regression",
+    "samplers/MLDA_gravity_surveying",
+    "samplers/MLDA_variance_reduction_linear_regression",
+    "samplers/GLM-hierarchical-jax",
+    "pymc3_howto/api_quickstart",
+    "variational_inference/variational_api_quickstart",
+    "Probability_Distributions.rst",
+    "pymc3_howto/data_container",
+    "pymc3_howto/sampling_compound_step",
+    "pymc3_howto/sampling_callback",
+    "diagnostics_and_criticism/sampler-stats",
+    "diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences",
+    "Advanced_usage_of_Theano_in_PyMC3.rst",
+    "ode_models/ODE_API_shapes_and_benchmarking",
+    "samplers/DEMetropolisZ_EfficiencyComparison",
+    "samplers/DEMetropolisZ_tune_drop_fraction",
+    "case_studies/factor_analysis",
+    "case_studies/blackbox_external_likelihood",
+    "pymc3_howto/profiling",
+    "pymc3_howto/howto_debugging",
+    "diagnostics_and_criticism/model_averaging",
+    "pymc3_howto/updating_priors",
+    "pymc3_howto/lasso_block_update",
+    "nb_examples/index.html",
+    "nb_tutorials/index.html",
+]
 rediraffe_redirects = {
     "index.md": "learn.md",
+    "pymc-examples/examples/getting_started.md": "learn/core_notebooks/pymc_overview.ipynb",
+    "pymc-examples/examples/PyMC3_and_Theano.rst": "learn/core_notebooks/pymc_pytensor.ipynb",
+    "pymc-examples/examples/generalized_linear_models/GLM.ipynb": "learn/core_notebooks/GLM_linear.ipynb",
+    "pymc-examples/examples/generalized_linear_models/GLM-linear.ipynb": "learn/core_notebooks/GLM_linear.ipynb",
+    "pymc-examples/examples/diagnostics_and_criticism/model_comparison.ipynb": "learn/core_notebooks/model_comparison.ipynb",
+    "pymc-examples/examples/diagnostics_and_criticism/posterior_predictive.ipynb": "learn/core_notebooks/posterior_predictive.ipynb",
+    "pymc-examples/examples/Gaussian_Processes.rst": "learn/core_notebooks/Gaussian_Processes.rst",
+    **{
+        f"pymc-examples/examples/{v3_path}.ipynb": "learn/core_notebooks/index.md"
+        for v3_path in v3_example_tutorials
+    },
 }
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

docs/source/learn/core_notebooks/index.md

@@ -12,3 +12,11 @@ dimensionality
 pymc_pytensor
 Gaussian_Processes
 :::
+
+:::{note}
+The notebooks above are executed with each version of the library
+(available on the navigation bar). In addition, a much larger gallery
+of example notebooks is available at the {doc}`"Examples" tab <nb:gallery>`.
+These are executed more sparsely and independently.
+They include a watermark to show which versions were used to run them.
+:::