Data depth inference with python

This readme will guide you through the use of the code in this repository.

The code in this repository is for nonparametric prior-free and likelihood-free posterior inference.

We named this method: Inference with consonant structures via data peeling

As the name suggests, this method construct consonant confidence structures directly from data using a procedure name data peeling.

When to use this code?

The probability distribution of the data-generating mechanism, $P_{X}$ is multivariate (d>2)
The distribution family (e.g. lognormal) of $P_{X}$ is unkown
$P_{X}$ is stationary
$X_{i}, i=1,…,n$ are iid samples drown from $P_{X}$
For backward propagation, i.e. $P_{X}$ is the distribution of an output quantity and inference is done on the inputs
When uncertainty quantification based solely on data is needed: e.g. computing failure probability based on data only