Increasing interpretability of Bayesian probabilistic programming models through interactive visualizations

Taka, E., Stein, S. and Williamson, J. H. (2020) Increasing interpretability of Bayesian probabilistic programming models through interactive visualizations. Frontiers in Computer Science, 2, 567344. (doi: 10.3389/fcomp.2020.567344)

[img] Text
226085.pdf - Published Version
Available under License Creative Commons Attribution.



Bayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling. However, the results of Bayesian inference are challenging for users to interpret in tasks like decision-making under uncertainty or model refinement. Decision-makers need simultaneous insight into both the model’s structure and its predictions, including uncertainty in inferred parameters. This enables better assessment of the risk overall possible outcomes compatible with observations and thus more informed decisions. To support this, we see a need for visualization tools that make probabilistic programs interpretable to reveal the interdependencies in probabilistic models and their inherent uncertainty. We propose the automatic transformation of Bayesian probabilistic models, expressed in a probabilistic programming language, into an interactive graphical representation of the model’s structure at varying levels of granularity, with seamless integration of uncertainty visualization. This interactive graphical representation supports the exploration of the prior and posterior distribution of MCMC samples. The interpretability of Bayesian probabilistic programming models is enhanced through the interactive probabilistic models explorer, which provides human users with more informative, transparent, and explainable probabilistic models. We present a concrete implementation that translates probabilistic programs to interactive graphical representation and show illustrative examples for a variety of Bayesian probabilistic models.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Stein, Dr Sebastian and Williamson, Dr John and Taka, Evdoxia
Authors: Taka, E., Stein, S., and Williamson, J. H.
College/School:College of Science and Engineering > School of Computing Science
College of Medical Veterinary and Life Sciences > School of Infection & Immunity
Journal Name:Frontiers in Computer Science
Publisher:Frontiers Media
ISSN (Online):2624-9898
Copyright Holders:Copyright © 2020 Taka, Stein and Williamson
First Published:First published in Frontiers in Computer Science 2:567344
Publisher Policy:Reproduced under a Creative Commons Licence

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science