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Learning-Bayesian-Statistics

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• 2 weeks ago last updated

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   Causal Inference · learning-bayesian-statistics bundle

   Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."
   1 install
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   Bayesian Workflow · learning-bayesian-statistics bundle

   Opinionated Bayesian modeling workflow with PyMC and ArviZ. Contains critical guardrails (nutpie sampler, prior/posterior predictive checks, LOO-PIT calibration, prior sensitivity checks, 94% HDI, non-centered parameterizations, reproducible seeds) that agents won't apply unprompted — always consult before writing Bayesian model code. Trigger on: building probabilistic/Bayesian models, prior elicitation, MCMC inference, convergence diagnostics (divergences, R-hat, ESS), model comparison (LOO-CV, ELPD, stacking weights), hierarchical/multilevel models, count regressions, logistic regression with uncertainty, prior sensitivity analysis, reporting Bayesian results, or mentions of PyMC, ArviZ, InferenceData, credible intervals, posterior distributions, shrinkage, uncertainty quantification. Also trigger for model comparison, diagnosing sampling problems, choosing priors, or presenting stats to non-technical audiences.
   1 install
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   Amortized Workflow · learning-bayesian-statistics bundle

   Opinionated amortized Bayesian workflow with BayesFlow for simulation-based inference (SBI). Contains critical guardrails that agents will usually not apply unprompted — always consult before writing BayesFlow code. Trigger on: simulation-based inference, amortized inference, approximate Bayesian inference, BayesFlow, neural posterior estimation, posterior amortization, simulator design, prior design for SBI, offline/online simulation pipelines, uncertainty quantification from simulators, structured data encoders (sets, time series, images), or mentions of BasicWorkflow, fit_online, fit_offline, fit_disk, flow matching, diffusion model, consistency model, normalizing flow, summary networks, adapters, or simulation budgets.
   3 installs