## Evaluating Probability Models

A video introduction on how to evaluate probability models using the statistical deviance. (link)

A video introduction on how to evaluate probability models using the statistical deviance. (link)

(link)

I’d like to share an introduction to my data science chalk talk series (video link, series link)

I have a new short video lecture to share: “Classification as Censored Regression.”

I recently shared a bit of the history of The Science of Data Analysis. I thought I would follow that up with a quick chalk talk titled “What is Statistics?” (link)

I am re-reading from the great statistician John W. Tukey’s paper: Tukey, John W. “The Future of Data Analysis.” Ann. Math. Statist. 33 (1962), no. 1, pp. 1–67. doi:10.1214/aoms/1177704711. https://projecteuclid.org/euclid.aoms/1177704711 I’ve taken the liberty of pulling out some quotes that are very relevant to the usual “data science is not […]

I am excited to share my new free video lecture: Estimating the Odds with Bayes’ Law. (link)

I’d like to share a new fee mini-lecture on avoiding Simpson’s Paradox when analyzing A/B test results.

Introduction We’ve been writing on the distribution density shapes expected for probability models in ROC (receiver operator characteristic) plots, double density plots, and normal/logit-normal densities frameworks. I thought I would re-approach the issue with a specific family of examples.

Our group has written a lot on calibration of models and even conditional calibration of models. In our last note we mentioned the possibility of “fully calibrated models.” This note is an example of a probability model that is calibrated in the traditional sense, but not fully calibrated in a […]