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 |