Quantile Regression with BART / Asymmetric Laplace distribution |
Bayesian Estimation Supersedes the T-Test / Example: Drug trial evaluation |
Generalized Extreme Value Distribution / Modelling & Prediction |
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 |
Factor analysis / Model / Alternative parametrization |
Factor analysis / Model / Direct implementation |
NBA Foul Analysis with Item Response Theory / Item Response Model / PyMC implementation |
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 |
Reliability Statistics and Predictive Calibration / Bayesian Modelling of Reliability Data / Direct PYMC implementation of Weibull Survival |
A Hierarchical model for Rugby prediction / Building of the model |
Bayesian Non-parametric Causal Inference / Causal Inference and Propensity Scores / Double/Debiased Machine Learning and Frisch-Waugh-Lovell / Applying Debiased ML Methods |
Bayesian Non-parametric Causal Inference / Causal Inference and Propensity Scores / Mediation Effects and Causal Structure |
Bayesian Non-parametric Causal Inference / Causal Inference and Propensity Scores / Non-Confounded Inference: NHEFS Data / Propensity Score Modelling |
Bayesian Non-parametric Causal Inference / Causal Inference and Propensity Scores / Non-Confounded Inference: NHEFS Data / Regression with Propensity Scores |
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 |
Interventional distributions and graph mutation with the do-operator / Three different causal DAGs |
Interventional distributions and graph mutation with the do-operator / What can we do with Bayesian inference? |
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 |
Regression discontinuity design analysis / Sharp regression discontinuity model |
Sampler Statistics / Multiple samplers |
Sampler Statistics |
Using Data Containers / Applied example: height of toddlers as a function of age |
Using Data Containers / Applied Example: Using MutableData as input to a binomial GLM |
Using Data Containers / Constant Data |
Using Data Containers / Constant Data / Named dimensions with data containers |
Using Data Containers / MutableData / Using MutableData container variables to fit the same model to several datasets |
Kronecker Structured Covariances / LatentKron / Example 1 |
Gaussian Process (GP) smoothing / Let’s describe the above GP-smoothing model in PyMC |
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 |
Discrete Choice and Random Utility Models / Choosing Crackers over Repeated Choices: Mixed Logit Model |
Discrete Choice and Random Utility Models / Experimental Model: Adding Correlation Structure |
Discrete Choice and Random Utility Models / Improved Model: Adding Alternative Specific Intercepts |
Discrete Choice and Random Utility Models / The Basic Model |
GLM: Negative Binomial Regression / Negative Binomial Regression / Create GLM Model |
Ordinal Scales and Survey Data / Fit a variety of Model Specifications / Bayesian Particularities |
Ordinal Scales and Survey Data / Liddell and Kruschke’s IMDB movie Ratings Data |
Out-Of-Sample Predictions / Define and Fit the Model |
GLM: Poisson Regression / Poisson Regression / 1. Manual method, create design matrices and manually specify model |
Setup / 4. Simple Linear Model with Robust Student-T Likelihood / 4.1 Specify Model |
Setup / 5. Linear Model with Custom Likelihood to Distinguish Outliers: Hogg Method / 5.1 Specify Model |
Setup / 3. Simple Linear Model with no Outlier Correction / 3.1 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 |
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 |
LKJ Cholesky Covariance Priors for Multivariate Normal Models |
Bayesian Missing Data Imputation / Bayesian Imputation |
Bayesian Missing Data Imputation / Hierarchical Structures and Data Imputation |
Bayesian Missing Data Imputation / Bayesian Imputation by Chained Equations / PyMC Imputation |
Using a “black box” likelihood function (numpy) / Comparison to equivalent PyMC distributions |
Bayesian copula estimation: Describing correlated joint distributions / PyMC models for copula and marginal estimation |
How to debug a model / Introduction / Bringing it all together |
How to debug a model / Introduction / Troubleshooting a toy PyMC model |
Automatic marginalization of discrete variables / Gaussian Mixture model |
Using ModelBuilder class for deploying PyMC models / Model builder class |
Using ModelBuilder class for deploying PyMC models / Standard syntax |
Splines / The model / Fit the model |
How to wrap a JAX function for use in PyMC / Wrapping the JAX function in PyTensor / Sampling with PyMC |
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 |
Dirichlet process mixtures for density estimation / Dirichlet process mixtures |
Gaussian Mixture Model |
ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Bayesian Inference with Gradients / Simulate with Pytensor Scan / Inference Using NUTs |
ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Bayesian Inference with Gradients / PyMC ODE Module / Inference with NUTS |
ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / PyMC Model Specification for Gradient-Free Bayesian Inference / PyMC Model |
Work flow / Step 2: Define the fine model |
Work flow / Step 3: Define a coarse model |
Old good Gaussian fit |
Faster Sampling with JAX and Numba |
Lasso regression with block updating |
Conditional Autoregressive (CAR) model / Writing some models in PyMC / Our first model: an independent random effects model |
Conditional Autoregressive (CAR) model / Writing some models in PyMC / Our second model: a spatial random effects model (with fixed spatial dependence) |
Conditional Autoregressive (CAR) model / Writing some models in PyMC / Our third model: a spatial random effects model, with unknown spatial dependence |
Demonstrating the BYM model on the New York City pedestrian accidents dataset / Specifying a BYM model with PyMC |
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 |
Frailty and Survival Regression Models / Accelerated Failure Time Models |
Frailty and Survival Regression Models / Fit Basic Cox Model with Fixed Effects |
Frailty and Survival Regression Models / Fit Model with Shared Frailty terms by Individual |
Bayesian Survival Analysis / Bayesian proportional hazards model |
Reparameterizing the Weibull Accelerated Failure Time Model / Parameterization 1 |
Reparameterizing the Weibull Accelerated Failure Time Model / Parameterization 2 |
Reparameterizing the Weibull Accelerated Failure Time Model / Parameterization 3 |
Analysis of An AR(1) Model in PyMC |
Analysis of An AR(1) Model in PyMC / Extension to AR(p) |
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 |
Multivariate Gaussian Random Walk / Model |
Bayesian Vector Autoregressive Models / Adding a Bayesian Twist: Hierarchical VARs |
Bayesian Vector Autoregressive Models / Handling Multiple Lags and Different Dimensions |
Non-Linear Change Trajectories / A Minimal Model |
Non-Linear Change Trajectories / Adding in Polynomial Time |
Non-Linear Change Trajectories / Behaviour over time |
Non-Linear Change Trajectories / Comparing Trajectories across Gender |
Modelling Change over Time. / Model controlling for Peer Effects |
Modelling Change over Time. / The Unconditional Mean Model |
Modelling Change over Time. / The Uncontrolled Effects of Parental Alcoholism |
Modelling Change over Time. / Unconditional Growth Model |
GLM: Mini-batch ADVI on hierarchical regression model |
Variational Inference: Bayesian Neural Networks / Bayesian Neural Networks in PyMC / Model specification |
Pathfinder Variational Inference |
Introduction to Variational Inference with PyMC / Minibatches |
Introduction to Variational Inference with PyMC / Multilabel logistic regression |