Bayesian Estimation Supersedes the T-Test / Example: Drug trial evaluation |

Generalized Extreme Value Distribution / Modelling & Prediction |

LKJ Cholesky Covariance Priors for Multivariate Normal Models |

Estimating parameters of a distribution from awkwardly binned data / Example 2: Parameter estimation with the other set of bins / Model specification |

Estimating parameters of a distribution from awkwardly binned data / Example 6: A non-normal distribution / Model specification |

Estimating parameters of a distribution from awkwardly binned data / Example 3: Parameter estimation with two bins together / Model Specification |

Estimating parameters of a distribution from awkwardly binned data / Example 4: Parameter estimation with continuous and binned measures / Model Specification |

Estimating parameters of a distribution from awkwardly binned data / Example 5: Hierarchical estimation / Model specification |

Estimating parameters of a distribution from awkwardly binned data / Example 5: Hierarchical estimation / Inspect posterior |

Estimating parameters of a distribution from awkwardly binned data / Example 1: Gaussian parameter estimation with one set of bins / Model specification |

Using a “black box” likelihood function (numpy) / Comparison to equivalent PyMC distributions |

Factor analysis / Model / Alternative parametrization |

Factor analysis / Model / Direct implementation |

NBA Foul Analysis with Item Response Theory / Item Response Model / PyMC implementation |

Bayesian mediation analysis / Define the PyMC3 model and conduct inference |

Bayesian mediation analysis / Double check with total effect only model |

Does the effect of training upon muscularity decrease with age? / Define the PyMC model and conduct inference |

A Primer on Bayesian Methods for Multilevel Modeling / Adding group-level predictors |

A Primer on Bayesian Methods for Multilevel Modeling / Conventional approaches |

A Primer on Bayesian Methods for Multilevel Modeling / Adding group-level predictors / Correlations among levels |

A Primer on Bayesian Methods for Multilevel Modeling / Non-centered Parameterization |

A Primer on Bayesian Methods for Multilevel Modeling / Partial pooling model |

A Primer on Bayesian Methods for Multilevel Modeling / Varying intercept and slope model |

A Primer on Bayesian Methods for Multilevel Modeling / Varying intercept model |

Probabilistic Matrix Factorization for Making Personalized Recommendations / Probabilistic Matrix Factorization |

Model building and expansion for golf putting / A new model |

Model building and expansion for golf putting / Logit model |

A Hierarchical model for Rugby prediction / Building of the model |

Splines / The model / Fit the model |

How to wrap a JAX function for use in PyMC / Wrapping the JAX function in Aesara / Sampling with PyMC |

Difference in differences / Bayesian difference in differences / PyMC model |

Counterfactual inference: calculating excess deaths due to COVID-19 / Causal inference disclaimer |

Counterfactual inference: calculating excess deaths due to COVID-19 / Modelling |

Interrupted time series analysis / Modelling |

Regression discontinuity design analysis / Sharp regression discontinuity model |

Sampler Statistics / Multiple samplers |

Sampler Statistics |

Kronecker Structured Covariances / LatentKron / Example 1 |

Multi-output Gaussian Processes: Coregionalization models using Hamadard product / Intrinsic Coregionalization Model (ICM) |

Multi-output Gaussian Processes: Coregionalization models using Hamadard product / Linear Coregionalization Model (LCM) |

Inference |

Binomial regression / Binomial regression model |

GLM: Negative Binomial Regression / Negative Binomial Regression / Create GLM Model |

GLM: Poisson Regression / Poisson Regression / 1. Manual method, create design matrices and manually specify model |

GLM: Robust Linear Regression / Robust Regression / Normal Likelihood |

Rolling Regression / Rolling regression |

Rolling Regression |

Simpson’s paradox and mixed models / Model 1: Basic linear regression / Build model |

Simpson’s paradox and mixed models / Model 2: Independent slopes and intercepts model |

Simpson’s paradox and mixed models / Model 3: Hierarchical regression |

Bayesian regression with truncated or censored data / Implementing truncated and censored regression models / Censored regression model |

Bayesian regression with truncated or censored data / The problem that truncated or censored regression solves |

Bayesian regression with truncated or censored data / Implementing truncated and censored regression models / Truncated regression model |

General API quickstart / 3. Inference / 3.2 Analyze sampling results |

General API quickstart / 2. Probability Distributions / Deterministic transforms |

General API quickstart / 2. Probability Distributions / Initialize Random Variables |

General API quickstart / 2. Probability Distributions / Lists of RVs / higher-dimensional RVs |

General API quickstart / 1. Model creation |

General API quickstart / 2. Probability Distributions / Observed Random Variables |

General API quickstart / 4. Posterior Predictive Sampling |

General API quickstart / 4.1 Predicting on hold-out data |

General API quickstart / 3. Inference / 3.1 Sampling |

General API quickstart / 2. Probability Distributions / Unobserved Random Variables |

General API quickstart / 3. Inference / 3.3 Variational inference |

How to debug a model / Introduction / Bringing it all together |

How to debug a model / Introduction / Troubleshooting a toy PyMC model |

Lasso regression with block updating |

Gaussian Mixture Model |

Work flow / Step 2: Define the fine model |

Work flow / Step 3: Define a coarse model |

Old good Gaussian fit |

Censored Data Models / Censored data models / Model 1 - Imputed Censored Model of Censored Data |

Censored Data Models / Censored data models / Model 2 - Unimputed Censored Model of Censored Data |

Censored Data Models / Uncensored Model |

Air passengers - Prophet-like model / Part 1: linear trend |

Air passengers - Prophet-like model / Part 2: enter seasonality |

Forecasting with Structural AR Timeseries / Prediction Step |

Forecasting with Structural AR Timeseries / Complicating the Picture / Specifying a Trend Model |

Forecasting with Structural AR Timeseries / Specifying the Model |

Forecasting with Structural AR Timeseries / Complicating the picture further / Specifying the Trend + Seasonal Model |

Forecasting with Structural AR Timeseries / Complicating the Picture / Wrapping our model into a function |

Variational Inference: Bayesian Neural Networks / Bayesian Neural Networks in PyMC / Model specification |

Pathfinder Variational Inference |