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