Automatic Generation of Probabilistic Programming from Time Series Data

Understand

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.