Automatic Bayesian Density Analysis
This work addresses the problem of making exploratory data analysis accessible to non-expert users by automating complex statistical tasks, though it appears incremental as it builds on existing Bayesian methods.
The paper tackles the challenge of automatic and unsupervised exploratory data analysis by introducing Automatic Bayesian Density Analysis (ABDA), which enables tasks like missing value estimation, anomaly detection, and accurate density estimation for mixed tabular data without requiring statistical expertise.
Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.