Sampling functions

Expand for references to pymc.sample

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

Factor analysis / Model / Alternative parametrization

Factor analysis / Model / Direct implementation

Hierarchical Partial Pooling / Approach

NBA Foul Analysis with Item Response Theory / Sampling and convergence

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 / 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

Inference

Binomial regression / Binomial regression model

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

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

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

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 / Gradient-Free Sampler Options / Metropolis Sampler

ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Bayesian Inference with Gradients / PyMC ODE Module / NUTs Inference

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

Expand for references to pymc.sample_posterior_predictive

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 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

Modeling Heteroscedasticity with BART / Model Specification

Estimating parameters of a distribution from awkwardly binned data / Example 1: Gaussian parameter estimation with one set of bins / Checks on model

Estimating parameters of a distribution from awkwardly binned data / Example 5: Hierarchical estimation / Posterior predictive checks

Estimating parameters of a distribution from awkwardly binned data / Example 6: A non-normal distribution / Posterior predictive checks

Estimating parameters of a distribution from awkwardly binned data / Example 3: Parameter estimation with two bins together / Posterior predictive checks

Estimating parameters of a distribution from awkwardly binned data / Example 4: Parameter estimation with continuous and binned measures / Posterior predictive checks

Estimating parameters of a distribution from awkwardly binned data / Example 2: Parameter estimation with the other set of bins / Posterior predictive checks

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

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 / Logit model

Reliability Statistics and Predictive Calibration / Bayesian Modelling of Reliability Data / Direct PYMC implementation of Weibull Survival

A Hierarchical model for Rugby prediction / Results

Splines / The model / Fit the model

Stochastic Volatility model / Fit Model

Difference in differences / Bayesian difference in differences / Posterior prediction

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

Counterfactual inference: calculating excess deaths due to COVID-19 / Posterior predictive check

Interrupted time series analysis / Counterfactual inference

Interrupted time series analysis / Posterior predictive check

Regression discontinuity design analysis / Counterfactual questions

Bayes Factors and Marginal Likelihood / Bayes factors and inference

Kronecker Structured Covariances / LatentKron / Example 1

Gaussian Processes: Latent Variable Implementation / Example 2: Classification

Gaussian Processes: Latent Variable Implementation / Example 1: Regression with Student-T distributed noise / Prediction using .conditional

Interpreting the results

Out-Of-Sample Predictions / Generate Out-Of-Sample Predictions

Out-Of-Sample Predictions / Model Decision Boundary

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

Simpson’s paradox and mixed models / Model 1: Basic linear regression / Visualisation

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

General API quickstart / 4. Posterior Predictive Sampling

General API quickstart / 4.1 Predicting on hold-out data

Using ModelBuilder class for deploying PyMC models / Standard syntax

Old good Gaussian fit

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

Variational Inference: Bayesian Neural Networks / Lets look at what the classifier has learned

Variational Inference: Bayesian Neural Networks / Bayesian Neural Networks in PyMC / Variational Inference: Scaling model complexity

Expand for references to pymc.sample_prior_predictive

Generalized Extreme Value Distribution / Prior Predictive Checks

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

Introduction to Bayesian A/B Testing / Bernoulli Conversions / Prior predictive checks

Introduction to Bayesian A/B Testing / Value Conversions

A Primer on Bayesian Methods for Multilevel Modeling / Conventional approaches

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

Reliability Statistics and Predictive Calibration / Bayesian Modelling of Reliability Data / Direct PYMC implementation of Weibull Survival

Splines / The model / Fit the model

Stochastic Volatility model / Checking the model

Counterfactual inference: calculating excess deaths due to COVID-19 / Prior predictive check

Interrupted time series analysis / Prior predictive check

Bayes Factors and Marginal Likelihood / Savage-Dickey Density Ratio

Using ModelBuilder class for deploying PyMC models / Standard syntax

ODE Lotka-Volterra With Bayesian Inference in Multiple Ways / Bayesian Inference with Gradients / Simulate with Pytensor Scan / Check Time Steps

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

Bayesian Vector Autoregressive Models / Adding a Bayesian Twist: Hierarchical VARs

Bayesian Vector Autoregressive Models / Handling Multiple Lags and Different Dimensions

Samplers

Expand for references to pymc.NUTS

Sampler Statistics

DEMetropolis and DEMetropolis(Z) Algorithm Comparisons / Experiment #3. Accuracy and Bias / 10 Dimensions

DEMetropolis and DEMetropolis(Z) Algorithm Comparisons / Experiment #1. 10-Dimensional Target Distribution

DEMetropolis and DEMetropolis(Z) Algorithm Comparisons / Experiment #2. 50-Dimensional Target Distribution

DEMetropolis and DEMetropolis(Z) Algorithm Comparisons / Experiment #3. Accuracy and Bias / 50 Dimensions

GLM: Mini-batch ADVI on hierarchical regression model

Expand for references to pymc.Metropolis

Sampler Statistics / Multiple samplers

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

Lasso regression with block updating

Compound Steps in Sampling / Compound steps

Compound Steps in Sampling / Specify compound steps

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

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

Expand for references to pymc.Slice

Lasso regression with block updating

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