# A Simple Sequential Algorithm for Approximating Bayesian Inference

- Elizabeth Bonawitz,
*University of California, Berkeley*
- Stephanie Denison,
*University of California, Berkeley*
- Annie Chen,
*University of California, Berkeley*
- Alison Gopnik,
*University of California, Berkeley*
- Thomas Griffiths,
*University of California, Berkeley*

## Abstract

People can apparently make surprisingly sophisticated inductive
inferences, despite the fact that there are constraints on cognitive resources
that would make performing exact Bayesian inference computationally intractable.
What algorithms could they be using to make this possible? We show that a simple
sequential algorithm, Win-Stay, Lose-Shift (WSLS), can be used to approximate
Bayesian inference, and is consistent with human behavior on a causal learning
task. This algorithm provides a new way to understand people's judgments and a
new efficient method for performing Bayesian inference.

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