mv-laplace 0.1.1

Multivariate Laplace Distribution Sampler

Multivariate Laplace Distribution

A sampler package from a Multivariate Laplace distrbrution.

Background

I've been exploring Quantitative Finance for awhile now, and one frustrating point is the lack of a good sampler for a Multivariate Laplace distribution. As such I re-purposed a multivariate normal, normal, and Laplace distribution to fill the role of a multivariate Laplace. This isn't a fully implemented scipy-like class, similar to my PertDist package, but just enough for me to spin up a custom MCMC routine and do some proper financial modeling.

Installation

This package is on PyPi, and can be installed using pip install mv-laplace

Usage

Unlike the scipy implementations this is based on, this distribution class provides sampling abilities only and no distributional parameters. The package is pretty straightforward to use:

from mv_laplace import MvLaplaceSampler

sampler = MvLaplaceSampler(loc, cov)
samples = sampler.sample(sample_size)

The input should be a pair of arrays, the first of which containing the location values (the means) in a vector of length M, and the second containing the covariance matrix in a MxM matrix. The return will be a NxM matrix, with N being the sample_size input on the sampler. I used Wikipedia for basic reference on terminology.

Disclaimer

I provide no guarentees that this package fits proper statistical robustness of how a multivariate Laplace is supposed to work, just that it seems to work for the use case I have identified. Use at your own risk.

Roadmap

• Figure out how to calculate summary information, and refactor API to a scipi-like interface
• Add a Multivariate Asymmetric Laplace distribution class

Version History

License

This project uses the GNU General Public License.

Short version: Have fun and use it for whatever, just make sure to attribute me for it (-: