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arXiv · 1607.00710

Automatic Generation of Probabilistic Programming from Time Series Data

Anh Tong·Jaesik Choi
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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.

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Anh Tong, Jaesik Choi. 2016-07-04. Automatic Generation of Probabilistic Programming from Time Series Data. https://arxiv.org/abs/1607.00710

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