# choix¶

`choix`

is a Python library that provides inference algorithms for models based
on Luce’s choice axiom. These probabilistic models can be used to explain and
predict outcomes of comparisons between items.

**Pairwise comparisons**: when the data consists of comparisons between two items, the model variant is usually referred to as the*Bradley-Terry*model. It is closely related to the Elo rating system used to rank chess players.**Partial rankings**: when the data consists of rankings over (a subset of) the items, the model variant is usually referred to as the*Plackett-Luce*model.**Top-1 lists**: another variation of the model arises when the data consists of discrete choices, i.e., we observe the selection of one item out of a subset of items.**Choices in a network**: when the data consists of counts of the number of visits to each node in a network, the model is known as the*Network Choice Model*.

`choix`

makes it easy to infer model parameters from these different types of
data, using a variety of algorithms:

- Luce Spectral Ranking
- Minorization-Maximization
- Rank Centrality
- Approximate Bayesian inference with expectation propagation

An easy way to get started is by exploring the notebooks!