Increasing Interpretability of Bayesian Probabilistic Programming Models Through Interactive Representations
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 mod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-12-01
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| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2020.567344/full |