  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.
