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 |