Giora Simchoni

July 31st, 2019

JSM 2019

This RPres/html is available at Github or at: http://bit.ly/dvi_jsm2019

- Graduated MSc Statistics from TAU in 2010
- Data Scientist (otherwise they won't hire me) subspecies Statistician
- 888, ebay, IBM, vFunction
- Blogger: Sex, Drugs and Data
- R/Python enthusiast: Github

bit.ly/dvi_jsm2019

- The key to understanding Visual Inference:
- A plot is a statistic
- Permute your data a few times, gather a few plots
- Judge your plot vs. the distribution of plots or run a survey
- Assumption-free, Parameter-free
- But how to present a distribution of plots?

bit.ly/dvi_jsm2019

bit.ly/dvi_jsm2019

bit.ly/dvi_jsm2019

bit.ly/dvi_jsm2019

- But it sure is good at Computer Vision
- My idea: give a neural network thousands of scatter plots (mosaic plots, swarm plots)
- Of varying linear correlation (Cramer's V, t statistic)
- Train it to predict correlation (not calculate!)
- If it's good (low MSE), show it the lineup
- Make it choose the scatter plot with the highest score

bit.ly/dvi_jsm2019

(Full code in my blog post Book'em Danno! and through References)