TY - Generic T1 - Uncertainty in Interactive WMDS Visualizations T2 - 2019 Symposium on Visualization in Data Science Posters Y1 - 2019 A1 - Lata Kodali A1 - Wenskovitch, John A1 - Nathan Wycoff A1 - House, Leanna A1 - North, Chris KW - poster AB - Visualizations are useful when learning from high-dimensional data. However, visualizations can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. In our work, we extend a framework called Bayesian Visual Analytics (BaVA) on a dimension reduction algorithm, Weighted Multidimensional Scaling (WMDS), to incorporate uncertainty as analysts explore data visually. BaVA-WMDS visualizations are interactive, and possible interactions include manipulating variable weights and/or the coordinates of the two-dimensional projection. Uncertainty exists in these visualizations on the variable weights, the user interactions, and the fit of WMDS. We quantify these uncertainties using Bayesian models exploring randomness in both coordinates and weights in a method we call Interactive Probabilistic WMDS (IP-WMDS). Specifically, we use posterior estimates to assess fit of WMDS, the range of motion of coordinates, as well as variability in weights. Visually, we display such uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in fitting a dimension reduction algorithm. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts. JF - 2019 Symposium on Visualization in Data Science Posters T3 - VDS'19 CY - Vancouver, BC, Canada ER -