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 graphical representations, which provide human users with more informative, transparent, and explainable probabilistic 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.
The documentation of cultural heritage has long been a useful guide for conservators and restorers. Following the latest advances in HW & SW technologies, the conservation science is keeping up the pace via the incorporation of state-ofthe-art digitized analysis to facilitate its modern challenges. In this direction, the current paper proposes a complete pipeline for augmenting the work of conservators by enhancing their intuition and the non-destructive requirements of it. In particular, the current study presents an end-to-end workflow that starts with the digitization of the shape of a mid-sized cultural object and simulates the degradation on its surface based on pre-trained material-specific aging models. The proposed system comprises a digitization interface that utilizes a low-cost RGB-D sensor registered with a rotary stage for the extraction of the global 3D textured model and a particle-based aging approach for the spatio-temporal simulation of the changes in both the appearance and structure. The simulation is controlled via both environmental (a.k.a. weathering phenomena) and material specific parameters. An original metallic sculpture has been used as a case study and the qualitative results presented in each intermediate processing step, demonstrate the overall functionality and the promising potential of the suggested approach.
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