Probabilistic Data Analysis with Probabilistic Programming
This work addresses the problem of complexity and interoperability in probabilistic data analysis for researchers and practitioners, offering a novel abstraction that is incremental in building upon existing probabilistic programming platforms.
The paper tackles the difficulty of applying, combining, and comparing probabilistic data analysis techniques by introducing composable generative population models (CGPMs), a computational abstraction that integrates into BayesDB, enabling tasks like identifying satellite data violations of Kepler's Third Law in under 50 lines of code and showing improved lines of code and accuracy compared to standard baselines.
Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.