0.8.0

Getting Started with NumPyro

API and Developer Reference

Pyro Primitives
Distributions
Inference
Effect Handlers
Contributed Code
Change Log

Introductory Tutorials

Bayesian Regression Using NumPyro
Bayesian Hierarchical Linear Regression
Example: Baseball Batting Average
Example: Variational Autoencoder
Example: Neal’s Funnel
Example: Stochastic Volatility
Example: ProdLDA with Flax and Haiku
Automatic rendering of NumPyro models
Bad posterior geometry and how to deal with it

Discrete Latent Variables

Example: Bayesian Models of Annotation
Example: Enumerate Hidden Markov Model
Example: CJS Capture-Recapture Model for Ecological Data
Example: Nested Sampling for Gaussian Shells
Bayesian Imputation for Missing Values in Discrete Covariates

Applications

Time Series Forecasting
Ordinal Regression
Bayesian Imputation
Example: Gaussian Process
Example: Bayesian Neural Network
Example: AutoDAIS
Example: Sparse Regression
Example: Horseshoe Regression
Example: Proportion Test
Example: Generalized Linear Mixed Models
Example: Hamiltonian Monte Carlo with Energy Conserving Subsampling
Example: Hidden Markov Model
Example: Hilbert space approximation for Gaussian processes.
Example: Predator-Prey Model
Example: Neural Transport
Example: MCMC Methods for Tall Data
Example: Thompson sampling for Bayesian Optimization with GPs

NumPyro

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Welcome to NumPyro Tutorials!
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Welcome to NumPyro Tutorials!¶

Tutorials:

Bayesian Regression Using NumPyro
Time Series Forecasting
- Data
- Model
- Inference
- Forecasting
- Acknowledgements
- References
Bayesian Imputation
- Dataset
- Prepare data
- Modelling
- Sampling
- Prediction
- References
Ordinal Regression
Bayesian Hierarchical Linear Regression
Bayesian Imputation for Missing Values in Discrete Covariates
- MAR conditional on outcome
- MNAR conditional on covariate
Automatic rendering of NumPyro models

Indices¶

Index

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