Bayesian Additive Regression Trees: Introduction / Biking with BART |

Bayesian Additive Regression Trees: Introduction / Coal mining with BART |

Bayesian Additive Regression Trees: Introduction / Biking with BART / Out-of-Sample Predictions / Regression |

Bayesian Additive Regression Trees: Introduction / Biking with BART / Out-of-Sample Predictions / Time Series |

Quantile Regression with BART / Asymmetric Laplace distribution |

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

Generalized Extreme Value Distribution / Inference |

LKJ Cholesky Covariance Priors for Multivariate Normal Models |

Bayesian Missing Data Imputation / Bayesian Imputation |

Bayesian Missing Data Imputation / Bayesian Imputation by Chained Equations / PyMC Imputation |

Modeling Heteroscedasticity with BART / Model Specification |

Introduction to Bayesian A/B Testing / Bernoulli Conversions / Data |

Introduction to Bayesian A/B Testing / Generalising to multi-variant tests |

Introduction to Bayesian A/B Testing / Value Conversions |

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 1: Gaussian parameter estimation with one set of bins / Model specification |

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

Using a “black box” likelihood function (numpy) / Introduction |

Using a “black box” likelihood function (numpy) / PyTensor Op with grad |

Using a “black box” likelihood function (numpy) / PyTensor Op without grad |

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 |

Factor analysis / Model / Alternative parametrization |

Factor analysis / Model / Direct implementation |

Hierarchical Partial Pooling / Approach |

NBA Foul Analysis with Item Response Theory / Sampling and convergence |

Non-Linear Change Trajectories / A Minimal Model |

Non-Linear Change Trajectories / Adding in Polynomial Time |

Non-Linear Change Trajectories / Behaviour over time |

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 |

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 |

Demonstrating the BYM model on the New York City pedestrian accidents dataset / Sampling the 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 / Fitting the distance angle model |

Model building and expansion for golf putting / Fitting the model on the new data |

Model building and expansion for golf putting / Logit model |

Model building and expansion for golf putting / Geometry-based model / Prior Predictive Checks |

Fitting a Reinforcement Learning Model to Behavioral Data with PyMC / Estimating the learning parameters via PyMC / Alternative model using Bernoulli for the likelihood |

Fitting a Reinforcement Learning Model to Behavioral Data with PyMC / Estimating the learning parameters via PyMC |

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 |

Splines / The model / Fit the model |

Stochastic Volatility model / Fit Model |

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

Difference in differences / Bayesian difference in differences / Inference |

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

Interrupted time series analysis / Inference |

Regression discontinuity design analysis / Inference |

Bayes Factors and Marginal Likelihood / Savage-Dickey Density Ratio |

Sampler Statistics / Multiple samplers |

Sampler Statistics |

Kronecker Structured Covariances / LatentKron / Example 1 |

Gaussian Processes: Latent Variable Implementation / Example 1: Regression with Student-T distributed noise / Coding the model in PyMC |

Gaussian Processes: Latent Variable Implementation / Example 2: Classification |

Marginal Likelihood Implementation / Example: Regression with white, Gaussian noise |

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 |

Hierarchical Binomial Model: Rat Tumor Example / Computing the Posterior using PyMC |

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 / 5. Linear Model with Custom Likelihood to Distinguish Outliers: Hogg Method / 5.2 Fit Model / 5.2.1 Sample Posterior |

Setup / 4. Simple Linear Model with Robust Student-T Likelihood / 4.2 Fit Model / 4.2.1 Sample Posterior |

Setup / 3. Simple Linear Model with no Outlier Correction / 3.2 Fit Model / 3.2.1 Sample Posterior |

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 / Do inference |

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 / Run the truncated and censored regressions |

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

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

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 |

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 |

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 |

Using ModelBuilder class for deploying PyMC models / Standard syntax |

Compound Steps in Sampling / Compound steps |

Compound Steps in Sampling / Compound steps by default |

Compound Steps in Sampling / Order of step methods |

Compound Steps in Sampling / Specify compound steps |

Dirichlet process mixtures for density estimation / Dirichlet process mixtures |

Gaussian Mixture Model |

ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Gradient-Free Sampler Options / DE MetropolisZ Sampler |

ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Gradient-Free Sampler Options / DEMetropolis Sampler |

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 / Gradient-Free Sampler Options / Metropolis Sampler |

ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Gradient-Free Sampler Options / Slice Sampler |

DEMetropolis and DEMetropolis(Z) Algorithm Comparisons / Helper Functions / Sampling |

DEMetropolis(Z) Sampler Tuning / Conclusions |

DEMetropolis(Z) Sampler Tuning / Helper Functions / Sampling |

Work flow / Step 4: Draw MCMC samples from the posterior using MLDA |

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 |

Bayesian Survival Analysis / Bayesian proportional hazards model |

Bayesian Survival Analysis / Bayesian proportional hazards model / Time varying effects |

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 / 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 / Handling Multiple Lags and Different Dimensions |

GLM: Mini-batch ADVI on hierarchical regression model |

Empirical Approximation overview / 2d density |

Empirical Approximation overview / Multimodal density |

Introduction to Variational Inference with PyMC / Basic setup |

Introduction to Variational Inference with PyMC / Distributional Approximations |