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PyMC Example Gallery#

Core notebooks#

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Introductory Overview of PyMC
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GLM: Linear regression
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Model Comparison
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Prior and Posterior Predictive Checks
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Distribution Dimensionality
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PyMC and Aesara

(Generalized) Linear and Hierarchical Linear Models#

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Bayesian regression with truncated or censored data

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Simpson’s paradox and mixed models

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Rolling Regression

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GLM: Robust Regression using Custom Likelihood for Outlier Classification

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GLM: Robust Linear Regression

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GLM: Poisson Regression

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Out-Of-Sample Predictions

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GLM: Negative Binomial Regression

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GLM: Model Selection

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Hierarchical Binomial Model: Rat Tumor Example

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Binomial regression

Case Studies#

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How to wrap a JAX function for use in PyMC

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Stochastic Volatility model

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Splines

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A Hierarchical model for Rugby prediction

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Reliability Statistics and Predictive Calibration

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Fitting a Reinforcement Learning Model to Behavioral Data with PyMC

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Model building and expansion for golf putting

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Probabilistic Matrix Factorization for Making Personalized Recommendations

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A Primer on Bayesian Methods for Multilevel Modeling

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Bayesian moderation analysis

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Bayesian mediation analysis

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NBA Foul Analysis with Item Response Theory

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Hierarchical Partial Pooling

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Factor analysis

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Conditional Autoregressive (CAR) model

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Using a “black box” likelihood function (numpy)

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Using a “black box” likelihood function (Cython)

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Estimating parameters of a distribution from awkwardly binned data

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Introduction to Bayesian A/B Testing

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Modeling Heteroscedasticity with BART

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Bayesian Missing Data Imputation

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LKJ Cholesky Covariance Priors for Multivariate Normal Models

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Generalized Extreme Value Distribution

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Bayesian Estimation Supersedes the T-Test

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Quantile Regression with BART

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Bayesian Additive Regression Trees: Introduction

Causal Inference#

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Regression discontinuity design analysis

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Interrupted time series analysis

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Counterfactual inference: calculating excess deaths due to COVID-19

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Difference in differences

Diagnostics and Model Criticism#

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Sampler Statistics

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Model Averaging

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Diagnosing Biased Inference with Divergences

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Bayes Factors and Marginal Likelihood

Gaussian Processes#

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Modeling spatial point patterns with a marked log-Gaussian Cox process

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Gaussian Processes using numpy kernel

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Multi-output Gaussian Processes: Coregionalization models using Hamadard product

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Gaussian Process (GP) smoothing

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Student-t Process

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Sparse Approximations

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Mean and Covariance Functions

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Example: Mauna Loa CO_2 continued

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Gaussian Process for CO2 at Mauna Loa

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Marginal Likelihood Implementation

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Gaussian Processes: Latent Variable Implementation

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Kronecker Structured Covariances

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Heteroskedastic Gaussian Processes

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GP-Circular

Inference in ODE models#

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Lotka-Volterra with manual gradients

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ODE Lotka-Volterra With Bayesian Inference in Multiple Ways

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pymc3.ode: Shapes and benchmarking

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GSoC 2019: Introduction of pymc3.ode API

MCMC#

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Sequential Monte Carlo

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Approximate Bayesian Computation

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Variance reduction in MLDA - Linear regression

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The MLDA sampler

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MLDA sampler: Introduction and resources

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Multilevel Gravity Survey with MLDA

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Using JAX for faster sampling

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DEMetropolis(Z) Sampler Tuning

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DEMetropolis and DEMetropolis(Z) Algorithm Comparisons

Mixture Models#

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Marginalized Gaussian Mixture Model

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Gaussian Mixture Model

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Dirichlet process mixtures for density estimation

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Dirichlet mixtures of multinomials

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Dependent density regression

Survival Analysis#

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Reparameterizing the Weibull Accelerated Failure Time Model

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Bayesian Survival Analysis

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Censored Data Models

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Bayesian Parametric Survival Analysis with PyMC3

Time Series#

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Bayesian Vector Autoregressive Models

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Multivariate Gaussian Random Walk

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Forecasting with Structural AR Timeseries

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Inferring parameters of SDEs using a Euler-Maruyama scheme

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Air passengers - Prophet-like model

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Analysis of An AR(1) Model in PyMC

Variational Inference#

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Introduction to Variational Inference with PyMC

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Pathfinder Variational Inference

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Empirical Approximation overview

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Variational Inference: Bayesian Neural Networks

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GLM: Mini-batch ADVI on hierarchical regression model

How to#

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Updating priors

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Using a custom step method for sampling from locally conjugate posterior distributions

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Compound Steps in Sampling

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Sample callback

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Profiling

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Using ModelBuilder class for deploying PyMC models

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Lasso regression with block updating

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How to debug a model

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Using shared variables (Data container adaptation)

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Defining a Custom Distribution in PyMC3

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General API quickstart

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PyMC example gallery

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Object use index

On this page
  • Core notebooks
  • (Generalized) Linear and Hierarchical Linear Models
  • Case Studies
  • Causal Inference
  • Diagnostics and Model Criticism
  • Gaussian Processes
  • Inference in ODE models
  • MCMC
  • Mixture Models
  • Survival Analysis
  • Time Series
  • Variational Inference
  • How to
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