1.9.1

Introductory Tutorials

Introduction to Pyro
Automatic rendering of Pyro models
Rendering deterministic variables
SVI Part I: An Introduction to Stochastic Variational Inference in Pyro
SVI Part II: Conditional Independence, Subsampling, and Amortization
SVI Part III: ELBO Gradient Estimators
SVI Part IV: Tips and Tricks

Practical Pyro and PyTorch

Bayesian Regression - Introduction (Part 1)
Bayesian Regression - Inference Algorithms (Part 2)
Tensor shapes in Pyro
Modules in Pyro
High-dimensional Bayesian workflow, with applications to SARS-CoV-2 strains
Interactive posterior predictives checks
Using the PyTorch JIT Compiler with Pyro
Example: using vanilla PyTorch to perform optimization in SVI
Example: distributed training via Horovod
Example: distributed training via PyTorch Lightning
SVI with a Normalizing Flow guide

Deep Generative Models

Variational Autoencoders
The Semi-Supervised VAE
Conditional Variational Auto-encoder
Normalizing Flows - Introduction
Variational Autoencoder with a Normalizing Flow prior
Deep Markov Model
Attend Infer Repeat
Example: Causal Effect VAE
Example: Sparse Gamma Deep Exponential Family
Probabilistic Topic Modeling
scANVI: Deep Generative Modeling for Single Cell Data with Pyro

Discrete Latent Variables

Inference with Discrete Latent Variables
Gaussian Mixture Model
Dirichlet Process Mixture Models in Pyro
Example: Toy Mixture Model With Discrete Enumeration
Example: Hidden Markov Models
Example: Capture-Recapture Models (CJS Models)
Example: hierarchical mixed-effect hidden Markov models
Example: Discrete Factor Graph Inference with Plated Einsum
Example: Amortized Latent Dirichlet Allocation

Customizing Inference

MLE and MAP Estimation
Doing the same thing with AutoGuides
Writing guides using EasyGuide
Customizing SVI objectives and training loops
Boosting Black Box Variational Inference
Example: Neural MCMC with NeuTraReparam
Example: Sparse Bayesian Linear Regression
Example: reducing boilerplate with pyro.contrib.autoname

Application: Time Series

Forecasting I: univariate, heavy tailed
Forecasting II: state space models
Forecasting III: hierarchical models
Forecasting with Dynamic Linear Model (DLM)
Levy Stable models of Stochastic Volatility
Multivariate Forecasting
Example: Gaussian Process Time Series Models

Application: Gaussian Processes

Gaussian Processes
Gaussian Process Latent Variable Model
Bayesian Optimization
Example: Deep Kernel Learning

Application: Epidemiology

Epidemiological models: Introduction
Example: Univariate epidemiological models
Example: Regional epidemiological models
Example: Epidemiological inference via HMC
Logistic growth models of SARS-CoV-2 lineage proportions

Application: Biological sequences

Example: Constant + MuE (Profile HMM)
Example: Probabilistic PCA + MuE (FactorMuE)

Application: Experimental Design

Designing Adaptive Experiments to Study Working Memory
Predicting the outcome of a US presidential election using Bayesian optimal experimental design

Application: Object Tracking

Tracking an Unknown Number of Objects
Kalman Filter

Other Inference Algorithms

Example: analyzing baseball stats with MCMC
Example: Inference with Markov Chain Monte Carlo
Example: MCMC with an LKJ prior over covariances
Compiled Sequential Importance Sampling
Example: Sequential Monte Carlo Filtering
Example: importance sampling
The Rational Speech Act framework
Understanding Hyperbole using RSA
Example: Utilizing Predictive and Deterministic with MCMC and SVI

Understanding Pyro's Internals

Mini-Pyro
Poutine: A Guide to Programming with Effect Handlers in Pyro
pyro.contrib.funsor, a new backend for Pyro - New primitives (Part 1)
pyro.contrib.funsor, a new backend for Pyro - Building inference algorithms (Part 2)
Example: hidden Markov models with pyro.contrib.funsor and pyroapi

Deprecated

(DEPRECATED) An Introduction to Models in Pyro
(DEPRECATED) An Introduction to Inference in Pyro

Pyro Tutorials

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Index

Index

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