Liesel: A Probabilistic Programming Framework#

Welcome to the API documentation of Liesel, a probabilistic programming framework with a focus on semi-parametric regression. It includes:

Liesel, a library to express statistical models as Probabilistic Graphical Models (PGMs). Through the PGM representation, the user can build and update models in a natural way.
Goose, a library to build custom MCMC algorithms with several parameter blocks and MCMC kernels such as the No U-Turn Sampler (NUTS), the Iteratively Weighted Least Squares (IWLS) sampler, or different Gibbs samplers. Goose also takes care of the MCMC bookkeeping and the chain post-processing.
RLiesel, an R interface for Liesel which assists the user with the configuration of semi-parametric regression models such as Generalized Additive Models for Location, Scale and Shape (GAMLSS) with different response distributions, spline-based smooth terms and shrinkage priors.

The name “Liesel” is an homage to the Gänseliesel fountain, landmark of Liesel’s birth city Göttingen.

Installation#

You can install Liesel via pip:

$ pip install liesel

If you want to work with the latest development version of Liesel or use PyGraphviz for prettier plots of the model graphs, see the README in the main repository.

Now you can get started. Throughout this documentation, we import Liesel as follows:

import liesel.model as lsl
import liesel.goose as gs

We also commonly use the following imports:

import jax
import jax.numpy as jnp
import numpy as np
import tensorflow_probability.substrates.jax.distributions as tfd

We provide overviews of the most important building blocks provided by liesel.model and liesel.goose in Model Building (liesel.model) and MCMC Sampling (liesel.goose), respectively.

Tutorials#

To start working with Liesel, our tutorials might come in handy, starting with a tutorial on linear regression. An overview of our tutorials can be found here: Liesel tutorials.

Acknowledgements#

Liesel is being developed by Paul Wiemann and Hannes Riebl at the University of Göttingen with support from Thomas Kneib. Important contributions were made by Joel Beck, Alex Afanasev, Gianmarco Callegher and Johannes Brachem. We are grateful to the German Research Foundation (DFG) for funding the development through grant 443179956.

API Reference#

`model`	Liesel modeling framework.
`goose`	Goose MCMC framework.
`contrib`	Additional functionality that does not belong to the core of the Liesel framework.
`distributions`	Extra distributions for JAX-TFP.
`bijectors`	Extra bijectors for JAX-TFP.

experimental

Experimental Liesel add-ons.

Effort-Based Versioning#

Starting with v0.4.0, we will be using effort-based versioning. See the EffVer documentation at https://jacobtomlinson.dev/effver/

The JAX developers provide a wonderful summary: https://docs.jax.dev/en/latest/jep/25516-effver.html

The following description is almost entirely quoted from the linked JAX page, but it describes what we intend with effort-based versioning perfectly.

Effort-based versioning is a three-number versioning system, similar to the better-known semantic versioning (SemVer: https://semver.org/). It uses a three-number format: MACRO.MESO.MICRO, where version numbers are incremented based on the expected effort required to adapt to the change.

As an example, consider software with current version 2.3.4:

Increasing the micro version (i.e. releasing 2.3.5) signals to users that little to no effort is necessary on their part to adapt to the changes.
Increasing the meso version (i.e. releasing 2.4.0) signals to users that some small effort will be required for existing code to work with the changes.
Increasing the macro version (i.e. releasing 3.0.0) signals to users that significant effort may be required to update to the changes.

In some ways, this captures the essence of more commonly-used semantic versioning, but avoids phrasing in terms of compatibility guarantees that are hard to meet in practice.

Zero Version#

In addition, EffVer gives special meaning to the zero version. Early releases of software are often versioned 0.X.Y, and in this case:

X has the characteristics of the macro version.
Y has the characteristics of the meso version.

Liesel has been in a zero-version state since its initial release, and EffVer’s zero-version case is a good post-facto description of the implicit intent behind Liesel’s releases to date.

In EffVer, bumping from 0.X.Y to version 1.0.0 is recommended when a certain level of stability has been reached in practice: If you end up on a version like 0.9.x for many months, it is a good signal that things are pretty stable and that it’s time to switch to a 1.0.0 release.

Liesel: A Probabilistic Programming Framework

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