# Recursive Bayes

**URL:** https://discourse.julialang.org/t/recursive-bayes/55036
**Category:** Probabilistic Programming
**Created:** [February 11, 2021, 12:00am UTC](https://discourse.julialang.org/t/recursive-bayes/55036 "2021-02-11T00:00:12Z")
**Posts on this page:** 1
**Showing post:** 2

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### Author: ![cscherrer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cscherrer/32/7631_2.png) [@cscherrer](https://discourse.julialang.org/u/cscherrer)
#### Post date: [February 11, 2021, 12:39am UTC](https://discourse.julialang.org/t/recursive-bayes/55036/2 "2021-02-11T00:39:02Z")

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It’s funny, this is such a standard use of Bayesian analysis, but most PPLs don’t have much support for it. It’s usually better to do inference for as much data as you have at a time, because the posterior is represented as a function of the data. There are two main exceptions to this:

- If you make strong assumptions about the data, e.g. for a Kalman filter you assume normality.
- If you make very few assumptions, and do things very empirically. Then you end up with a particle filter.

One big focus of [Soss.jl](https://github.com/cscherrer/Soss.jl) is model composability, in particular models can chain together. There’s an example [here](https://discourse.julialang.org/t/sir-modeling-in-soss/36680) of building a Markov chain in Soss. This kind of thing will get easier with some new updates in the works. What (I think) we really want is something like

- Represent a chain abstractly
- Observe a chunk of data, and get the posterior conditional all this (all at once)
- Turn this into a representation of a new distribution to be used as a prior. Maybe it’s a Gaussian approximation, or a mixture of Gaussians, or an approximation from some variational family.

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