Automatic Generation of Probabilistic Programming from Time Series Data
Abstract
Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks to compute interesting probabilities of various large, real-world problems. When the structure of model is given, constructing a probabilistic program is rather straightforward. Thus, main focus have been to learn the best model parameters and compute marginal probabilities. In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given. The intuition behind of our method is to find a descriptive covariance structure of time series data in nonparametric Gaussian process regression. We report that such descriptive covariance structure efficiently derives a probabilistic programming description accurately.
Cite
@article{arxiv.1607.00710,
title = {Automatic Generation of Probabilistic Programming from Time Series Data},
author = {Anh Tong and Jaesik Choi},
journal= {arXiv preprint arXiv:1607.00710},
year = {2016}
}