Stanford Seminar - Theories of Inference for Visual Analysis

This seminar examines how visualizations shape statistical inference in exploratory analysis. It covers uncertainty representation, human belief updating, causal inference, and graphical model checks, including a Tableau-like system for supporting model checking.

Syllabus

Introduction.
Visualizations help offload cognition to perception.
State-of-the-art: Optimize for easy pattern finding.
Approaches to visualize distribution or uncertainty.
Error bars/intervals violate expressiveness.
Probability encodings often hard to read accurately.
Frequency based uncertainty visualization.
Evaluation: Effect size judgments.
Mean difference heuristic in effect size judgments.
Sensitivity to visual distance between means.
Investigating the mean difference heuristic.
Uncertainty visualization conditions.
Bayesian cognition to understand belief updating.
How well do visualizations support causal inference?.
How does visualization support learning in analysis?.
Good visualizations facilitate model checks.
Implicit model checks give graphs their meaning.
Graphical inference as Bayesian model check.
Tableau-like system supporting model checks.
From visualization to implied model.
Open questions.
What characterizes good inference problems for vis?.
Bounding the value of a more expressive visualization.