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A visual analytics workflow for probabilistic modeling
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DOI：10.1016/j.visinf.2023.05.001.png)
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Abstract
En 中文
Probabilistic programming is a powerful means for formally specifying machine learning models. The inference engine of a probabilistic programming environment can be used for serving complex queries on these models. Most of the current research in probabilistic programming is dedicated to the design and implementation of highly efficient inference engines. Much less research aims at making the power of these inference engines accessible to non-expert users. Probabilistic programming means writing code. Yet many potential users from promising application areas such as the social sciences lack programming skills. This prompted recent efforts in synthesizing probabilistic programs directly from data. However, working with synthesized programs still requires the user to read, understand, and write some code, for instance, when invoking the inference engine for answering queries. Here, we present an interactive visual approach to synthesizing and querying probabilistic programs that does not require the user to read or write code.& COPY; 2023 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
Probabilistic inference
Bayesian network
Structured query language
Table-based visualization
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