PyMC Example Gallery# Introductory# General Overview Introductory Overview of PyMC Simple Linear Regression GLM: Linear regression General API quickstart General API quickstart Library Fundamentals# Distribution Dimensionality Distribution Dimensionality PyMC and PyTensor PyMC and PyTensor Using Data Containers Using Data Containers How to# Prior and Posterior Predictive Checks Prior and Posterior Predictive Checks Model Comparison Model comparison LKJ Cholesky Covariance Priors for Multivariate Normal Models LKJ Cholesky Covariance Priors for Multivariate Normal Models Bayesian Missing Data Imputation Bayesian Missing Data Imputation Using a “black box” likelihood function Using a “black box” likelihood function Bayesian copula estimation: Describing correlated joint distributions Bayesian copula estimation: Describing correlated joint distributions How to debug a model How to debug a model Bayesian Hypothesis Testing - an introduction Bayesian Hypothesis Testing - an introduction Automatic marginalization of discrete variables Automatic marginalization of discrete variables Using ModelBuilder class for deploying PyMC models Using ModelBuilder class for deploying PyMC models Profiling Profiling Splines Splines Updating Priors Updating Priors How to wrap a JAX function for use in PyMC How to wrap a JAX function for use in PyMC Generalized Linear Models# Binomial regression Binomial regression Discrete Choice and Random Utility Models Discrete Choice and Random Utility Models Hierarchical Binomial Model: Rat Tumor Example Hierarchical Binomial Model: Rat Tumor Example GLM-missing-values-in-covariates GLM-missing-values-in-covariates GLM: Model Selection GLM: Model Selection GLM: Negative Binomial Regression GLM: Negative Binomial Regression GLM-ordinal-features GLM-ordinal-features Regression Models with Ordered Categorical Outcomes Regression Models with Ordered Categorical Outcomes Out-Of-Sample Predictions Out-Of-Sample Predictions GLM: Poisson Regression GLM: Poisson Regression GLM: Robust Regression using Custom Likelihood for Outlier Classification GLM: Robust Regression using Custom Likelihood for Outlier Classification GLM: Robust Linear Regression GLM: Robust Linear Regression Rolling Regression Rolling Regression Bayesian regression with truncated or censored data Bayesian regression with truncated or censored data A Primer on Bayesian Methods for Multilevel Modeling A Primer on Bayesian Methods for Multilevel Modeling Case Studies# Bayesian Estimation Supersedes the T-Test Bayesian Estimation Supersedes the T-Test Confirmatory Factor Analysis and Structural Equation Models in Psychometrics Confirmatory Factor Analysis and Structural Equation Models in Psychometrics Generalized Extreme Value Distribution Generalized Extreme Value Distribution Bayesian Workflow with SEMs Bayesian Workflow with SEMs The Bayesian Workflow: COVID-19 Outbreak Modeling The Bayesian Workflow: COVID-19 Outbreak Modeling Estimating parameters of a distribution from awkwardly binned data Estimating parameters of a distribution from awkwardly binned data Factor analysis Factor analysis Hierarchical Partial Pooling Hierarchical Partial Pooling NBA Foul Analysis with Item Response Theory NBA Foul Analysis with Item Response Theory Probabilistic Matrix Factorization for Making Personalized Recommendations Probabilistic Matrix Factorization for Making Personalized Recommendations Model building and expansion for golf putting Model building and expansion for golf putting Fitting a Reinforcement Learning Model to Behavioral Data with PyMC Fitting a Reinforcement Learning Model to Behavioral Data with PyMC Reliability Statistics and Predictive Calibration Reliability Statistics and Predictive Calibration A Hierarchical model for Rugby prediction A Hierarchical model for Rugby prediction Forecasting Hurricane Trajectories with State Space Models Forecasting Hurricane Trajectories with State Space Models Causal Inference# Simpson’s paradox Simpson’s paradox Introduction to Bayesian A/B Testing Introduction to Bayesian A/B Testing Bayesian Non-parametric Causal Inference Bayesian Non-parametric Causal Inference Difference in differences Difference in differences Counterfactual inference: calculating excess deaths due to COVID-19 Counterfactual inference: calculating excess deaths due to COVID-19 Interrupted time series analysis Interrupted time series analysis Interventional distributions and graph mutation with the do-operator Interventional distributions and graph mutation with the do-operator Bayesian mediation analysis Bayesian mediation analysis Bayesian moderation analysis Bayesian moderation analysis Regression discontinuity design analysis Regression discontinuity design analysis Gaussian Processes# Baby Births Modelling with HSGPs Baby Births Modelling with HSGPs GP-Circular GP-Circular Heteroskedastic Gaussian Processes Heteroskedastic Gaussian Processes Kronecker Structured Covariances Kronecker Structured Covariances Gaussian Processes: Latent Variable Implementation Gaussian Processes: Latent Variable Implementation Marginal Likelihood Implementation Marginal Likelihood Implementation Gaussian Process for CO2 at Mauna Loa Gaussian Process for CO2 at Mauna Loa Example: Mauna Loa CO_2 continued Example: Mauna Loa CO_2 continued Mean and Covariance Functions Mean and Covariance Functions Sparse Approximations Sparse Approximations Student-t Process Student-t Process Gaussian Process (GP) smoothing Gaussian Process (GP) smoothing Gaussian Processes: HSGP Advanced Usage Gaussian Processes: HSGP Advanced Usage Gaussian Processes: HSGP Reference & First Steps Gaussian Processes: HSGP Reference & First Steps Multi-output Gaussian Processes: Coregionalization models using Hamadard product Multi-output Gaussian Processes: Coregionalization models using Hamadard product Gaussian Processes using numpy kernel Gaussian Processes using numpy kernel Modeling spatial point patterns with a marked log-Gaussian Cox process Modeling spatial point patterns with a marked log-Gaussian Cox process Time Series# Analysis of An AR(1) Model in PyMC Analysis of An AR(1) Model in PyMC Air passengers - Prophet-like model Air passengers - Prophet-like model Inferring parameters of SDEs using a Euler-Maruyama scheme Inferring parameters of SDEs using a Euler-Maruyama scheme Forecasting with Structural AR Timeseries Forecasting with Structural AR Timeseries Multivariate Gaussian Random Walk Multivariate Gaussian Random Walk Time Series Models Derived From a Generative Graph Time Series Models Derived From a Generative Graph Bayesian Vector Autoregressive Models Bayesian Vector Autoregressive Models Longitudinal Models of Change Longitudinal Models of Change Stochastic Volatility model Stochastic Volatility model Spatial Analysis# Conditional Autoregressive (CAR) Models for Spatial Data Conditional Autoregressive (CAR) Models for Spatial Data The prevalence of malaria in the Gambia The prevalence of malaria in the Gambia The Besag-York-Mollie Model for Spatial Data The Besag-York-Mollie Model for Spatial Data Diagnostics and Model Criticism# Bayes Factors and Marginal Likelihood Bayes Factors and Marginal Likelihood Diagnosing Biased Inference with Divergences Diagnosing Biased Inference with Divergences Model Averaging Model Averaging Sampler Statistics Sampler Statistics Bayesian Additive Regression Trees# Categorical regression Categorical regression Modeling Heteroscedasticity with BART Modeling Heteroscedasticity with BART Bayesian Additive Regression Trees: Introduction Bayesian Additive Regression Trees: Introduction Quantile Regression with BART Quantile Regression with BART Mixture Models# Dependent density regression Dependent density regression Dirichlet mixtures of multinomials Dirichlet mixtures of multinomials Dirichlet process mixtures for density estimation Dirichlet process mixtures for density estimation Gaussian Mixture Model Gaussian Mixture Model Marginalized Gaussian Mixture Model Marginalized Gaussian Mixture Model Survival Analysis# Bayesian Parametric Survival Analysis Bayesian Parametric Survival Analysis Censored Data Models Censored Data Models Frailty and Survival Regression Models Frailty and Survival Regression Models Bayesian Survival Analysis Bayesian Survival Analysis Reparameterizing the Weibull Accelerated Failure Time Model Reparameterizing the Weibull Accelerated Failure Time Model ODE models# GSoC 2019: Introduction of pymc3.ode API GSoC 2019: Introduction of pymc3.ode API pymc3.ode: Shapes and benchmarking pymc3.ode: Shapes and benchmarking ODE Lotka-Volterra With Bayesian Inference in Multiple Ways ODE Lotka-Volterra With Bayesian Inference in Multiple Ways Lotka-Volterra with manual gradients Lotka-Volterra with manual gradients MCMC# DEMetropolis and DEMetropolis(Z) Algorithm Comparisons DEMetropolis and DEMetropolis(Z) Algorithm Comparisons DEMetropolis(Z) Sampler Tuning DEMetropolis(Z) Sampler Tuning Approximate Bayesian Computation Approximate Bayesian Computation Sequential Monte Carlo Sequential Monte Carlo Faster Sampling with JAX and Numba Faster Sampling with JAX and Numba Lasso regression with block updating Lasso regression with block updating Compound Steps in Sampling Compound Steps in Sampling Using a custom step method for sampling from locally conjugate posterior distributions Using a custom step method for sampling from locally conjugate posterior distributions Variational Inference# GLM: Mini-batch ADVI on hierarchical regression model GLM: Mini-batch ADVI on hierarchical regression model Variational Inference: Bayesian Neural Networks Variational Inference: Bayesian Neural Networks Empirical Approximation overview Empirical Approximation overview Pathfinder Variational Inference Pathfinder Variational Inference Introduction to Variational Inference with PyMC Introduction to Variational Inference with PyMC Statistical Rethinking Lectures# The Garden of Forking Data The Garden of Forking Data Geocentric Models Geocentric Models Categories and Curves Categories and Curves Elemental Confounds Elemental Confounds Good & Bad Controls Good & Bad Controls Fitting Over & Under Fitting Over & Under Markov Chain Monte Carlo Markov Chain Monte Carlo Modeling Events Modeling Events Counts and Hidden Confounds Counts and Hidden Confounds Ordered Categories Ordered Categories Multilevel Models Multilevel Models Multilevel Adventures Multilevel Adventures Correlated Features Correlated Features Social Networks Social Networks Gaussian Processes Gaussian Processes Measurement and Misclassification Measurement and Misclassification Missing Data Missing Data Generalized Linear Madness Generalized Linear Madness Horoscopes Horoscopes
