Continuous#

Expand for references to pymc.Beta

Introduction to Bayesian A/B Testing / Bernoulli Conversions

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

Introduction to Bayesian A/B Testing / Value Conversions

Hierarchical Partial Pooling / Approach

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

Bayes Factors and Marginal Likelihood / Savage-Dickey Density Ratio

Bayes Factors and Marginal Likelihood / Computing Bayes factors / Sequential Monte Carlo

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

Compound Steps in Sampling / Compound steps by default

Dirichlet process mixtures for density estimation / Dirichlet process mixtures

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

Expand for references to pymc.Exponential

Bayesian Additive Regression Trees: Introduction / Biking 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

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

Hierarchical Partial Pooling / Approach

A Primer on Bayesian Methods for Multilevel Modeling / Conventional approaches

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

Splines / The model / Fit the model

Stochastic Volatility model / Build Model

Gaussian Processes: Latent Variable Implementation / Example 2: Classification

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

Rolling Regression / Rolling regression

Lasso regression with block updating

Analysis of An AR(1) Model in PyMC

Introduction to Variational Inference with PyMC / Distributional Approximations

Expand for references to pymc.Normal

Quantile Regression with BART / Asymmetric Laplace distribution

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

Generalized Extreme Value Distribution / Modelling & Prediction

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

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

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

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 / 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

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

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

Using ModelBuilder class for deploying PyMC models / Model builder class

Using ModelBuilder class for deploying PyMC models / Standard syntax

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 / NUTs Inference

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

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

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

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