Probabilistic modelling needs specialized tools to support modelers, decision-makers or researchers in the design, checking, refinement and communication of models. Users' comprehension of probabilistic models is vital in all above cases and interactive visualisations could enhance it. Although there are various studies evaluating interactivity in Bayesian reasoning and available tools for visualizing the inference-related distributions, we focus specifically on evaluating the effect of interaction on users' comprehension of probabilistic models' structure. We conducted a user study based on our Interactive Pair Plot for visualizing models' distribution and conditioning sample space graphically. Our results suggest that improvements in the understanding of the interactive group are most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterisations in comparison to the static group. As the detail of the inferred information increases, interaction does not lead to considerably longer response times. Finally, interaction improves users' confidence.
翻译:概率建模需要专门的工具来支持模型设计、检查、完善和交流模型的模型、决策者或研究人员。用户对概率模型的理解在以上所有情况下都至关重要,互动直观可以加强它。虽然有各种研究评估巴伊西亚推理中的相互作用以及可视推论相关分布的工具,但我们特别侧重于评价相互作用对用户理解概率模型结构的影响。我们根据我们的交互式平方图进行了一项用户研究,以图像化模型分布和对样板空间进行图形化调整。我们的研究结果表明,对互动组的理解的改进对于更奇特的结构最为明显,例如等级模型或与静态组相比不熟悉的参数化。随着推断信息的细节增加,互动不会导致更长的反应时间。最后,互动提高了用户的信心。