PyMC Example Gallery# Core notebooks# Introductory Overview of PyMC GLM: Linear regression Model Comparison Prior and Posterior Predictive Checks Distribution Dimensionality PyMC and Aesara (Generalized) Linear and Hierarchical Linear Models# Bayesian regression with truncated or censored data Simpson’s paradox and mixed models Rolling Regression GLM: Robust Regression using Custom Likelihood for Outlier Classification GLM: Robust Linear Regression GLM: Poisson Regression Out-Of-Sample Predictions GLM: Negative Binomial Regression GLM: Model Selection Hierarchical Binomial Model: Rat Tumor Example Binomial regression Case Studies# How to wrap a JAX function for use in PyMC Stochastic Volatility model Splines A Hierarchical model for Rugby prediction Reliability Statistics and Predictive Calibration Fitting a Reinforcement Learning Model to Behavioral Data with PyMC Model building and expansion for golf putting Probabilistic Matrix Factorization for Making Personalized Recommendations A Primer on Bayesian Methods for Multilevel Modeling Bayesian moderation analysis Bayesian mediation analysis NBA Foul Analysis with Item Response Theory Hierarchical Partial Pooling Factor analysis Conditional Autoregressive (CAR) model Using a “black box” likelihood function (numpy) Using a “black box” likelihood function (Cython) Estimating parameters of a distribution from awkwardly binned data Introduction to Bayesian A/B Testing Modeling Heteroscedasticity with BART Bayesian Missing Data Imputation LKJ Cholesky Covariance Priors for Multivariate Normal Models Generalized Extreme Value Distribution Bayesian Estimation Supersedes the T-Test Quantile Regression with BART Bayesian Additive Regression Trees: Introduction Causal Inference# Regression discontinuity design analysis Interrupted time series analysis Counterfactual inference: calculating excess deaths due to COVID-19 Difference in differences Diagnostics and Model Criticism# Sampler Statistics Model Averaging Diagnosing Biased Inference with Divergences Bayes Factors and Marginal Likelihood Gaussian Processes# Modeling spatial point patterns with a marked log-Gaussian Cox process Gaussian Processes using numpy kernel Multi-output Gaussian Processes: Coregionalization models using Hamadard product Gaussian Process (GP) smoothing Student-t Process Sparse Approximations Mean and Covariance Functions Example: Mauna Loa CO_2 continued Gaussian Process for CO2 at Mauna Loa Marginal Likelihood Implementation Gaussian Processes: Latent Variable Implementation Kronecker Structured Covariances Heteroskedastic Gaussian Processes GP-Circular Inference in ODE models# Lotka-Volterra with manual gradients ODE Lotka-Volterra With Bayesian Inference in Multiple Ways pymc3.ode: Shapes and benchmarking GSoC 2019: Introduction of pymc3.ode API MCMC# Sequential Monte Carlo Approximate Bayesian Computation Variance reduction in MLDA - Linear regression The MLDA sampler MLDA sampler: Introduction and resources Multilevel Gravity Survey with MLDA Using JAX for faster sampling DEMetropolis(Z) Sampler Tuning DEMetropolis and DEMetropolis(Z) Algorithm Comparisons Mixture Models# Marginalized Gaussian Mixture Model Gaussian Mixture Model Dirichlet process mixtures for density estimation Dirichlet mixtures of multinomials Dependent density regression Survival Analysis# Reparameterizing the Weibull Accelerated Failure Time Model Bayesian Survival Analysis Censored Data Models Bayesian Parametric Survival Analysis with PyMC3 Time Series# Bayesian Vector Autoregressive Models Multivariate Gaussian Random Walk Forecasting with Structural AR Timeseries Inferring parameters of SDEs using a Euler-Maruyama scheme Air passengers - Prophet-like model Analysis of An AR(1) Model in PyMC Variational Inference# Introduction to Variational Inference with PyMC Pathfinder Variational Inference Empirical Approximation overview Variational Inference: Bayesian Neural Networks GLM: Mini-batch ADVI on hierarchical regression model How to# Updating priors Using a custom step method for sampling from locally conjugate posterior distributions Compound Steps in Sampling Sample callback Profiling Using ModelBuilder class for deploying PyMC models Lasso regression with block updating How to debug a model Using shared variables (Data container adaptation) Defining a Custom Distribution in PyMC3 General API quickstart
