Abstract
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 programsinterpretableto 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 graphical representations, which provide human users with moreinformative, transparent, andexplainableprobabilistic models. We present a concrete implementation that translates probabilistic programs to interactive graphical representations and show illustrative examples for a variety of Bayesian probabilistic models.
Highlights
Bayesian probabilistic modeling has many advantages; it accounts for and represents uncertainty systematically; it allows precise incorporation of prior expert knowledge; and the intrinsic structure of models is well-defined in terms of relations among random variables: the mathematical and statistical dependencies are explicitly stated
Flexible Bayesian probabilistic models can be implemented via Probabilistic Programming Languages (PPLs), which provide automatic inference via practical and efficient Markov Chain Monte Carlo (MCMC) sampling
Data-scientists and statisticians seeking to refine and validate an inference process; in this work we propose a novel representation of Bayesian probabilistic models, the interactive probabilistic models explorer
Summary
Bayesian probabilistic modeling has many advantages; it accounts for and represents uncertainty systematically; it allows precise incorporation of prior expert knowledge; and the intrinsic structure of models is well-defined in terms of relations among random variables: the mathematical and statistical dependencies are explicitly stated. A very simple probabilistic model with few parameters could allow a human decision-maker to contemplate the entire model at once and comprehend how parameters interact with each other and the predictions of the model. This becomes more challenging as the model becomes more complex, perhaps with hierarchical structure, multivariate distributions, complex inter-dependencies and increasingly abstract latent states. Rational decisions should be based upon assessment of the risk of all possible states compatible with data; this is the key advantage of a Bayesian formulation This requires authentic representation of the parameter uncertainty. We propose that it is essential to communicate the (conditional) uncertainty of the parameters alongside their dependency structure
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