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