https://github.com/junpenglao/functionalbayes

A Functional Programming Approach to Composable Bayesian Workflow
https://github.com/junpenglao/functionalbayes

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A Functional Programming Approach to Composable Bayesian Workflow

• Host: GitHub
• URL: https://github.com/junpenglao/functionalbayes
• Owner: junpenglao
• License: mit
• Created: 2023-02-24T13:57:30.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-01T10:20:59.000Z (5 months ago)
• Last Synced: 2024-08-01T11:50:01.426Z (5 months ago)
• Language: Jupyter Notebook
• Size: 26.4 MB
• Stars: 7
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# A Functional Programming Approach to Composable Bayesian Workflow

Contributed talk at [Bayes Comp 2023](https://bayescomp2023.com/)

_Abstract_:

Bayesian modeling in practice is an iterative process, in which a practitioner implicitly or explicitly follows the [Bayesian workflow](https://arxiv.org/abs/2011.01808) (Gelman et al 2020) to build models and inferences that are closest to the “reality” within the computational constraints. A composable model building capability is often desired as it makes developing bigger and more complex Bayesian models easier: for example, changing the priors of a collection of random variables. Moreover, a composable approach could enable more flexibility in constructing inferences that optimize for local model structure, thus have the opportunity to improve inference quality compared to using a general inference methods statistical packages offer (e.g., NUTS with different schemes of adaptation). In this talk, I will explain how adopting a functional programming perspective benefits the development of composable Bayesian modeling and programmable inference, with example using [TensorFlow Probability on JAX](https://www.tensorflow.org/probability/examples/TensorFlow_Probability_on_JAX) (for the modeling part) and [Blackjax](https://blackjax-devs.github.io/blackjax/) (for the inference part).

## Set up

```shell

conda create -n bayescomp23 python=3.10

conda activate bayescomp23

pip install -r requirements.txt

```

## Slides

[Google Slides link](https://docs.google.com/presentation/d/1Fa2QEeFTo22AatybSiu9MeA6bF1kRq5JGVsF4W6BHa4/edit?usp=sharing&resourcekey=0-8DO5WGkp59q-K9RW7YUwSw)

## Materials

- [golf_putting.ipynb](https://github.com/junpenglao/functionalbayes/blob/main/golf_putting.ipynb): a notebook that demonstrates the iterative process of model building in a Bayesian workflow, with a functional programming princple to achieve composablity.

- [sparse_regression.ipynb](https://github.com/junpenglao/functionalbayes/blob/main/sparse_regression.ipynb): a simulation study with a sparse regression model, with a similar functional programming approch.

- [blackjax_deepdive.ipynb](https://github.com/junpenglao/functionalbayes/blob/main/blackjax_deepdive.ipynb): a notebook that demonstrates the use of low level Blackjax to diagnose the performance of sampling routine.