Tags » Bayesian

Bayesian optimization with scikit-learn

@tachyeonz : Choosing the right parameters for a machine learning model is almost more of an art than a science. Kaggle competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. 16 more words


Ultimate Game Theory

An introduction to the melted, gooey mind of a post-finals PhD student

In the days preceding my game theory final, I was quarantined in my Cambridge apartment. 4,038 more words


Bayesian modeling and prediction for movies

Part 1: Data

Movies are a compelling artform. But while nearly everyone enjoys a good movie, what is it exactly that makes a movie popular? To begin to answer this question, 651 movies were drawn at random from the… 1,796 more words


Zero-Numerator Problem: Calculating the Expected Number of Mistakes in Data Entry Jobs

I have a project for which I need to digitize a series of tables from scanned pdf pages. Due to the scan quality, some pages are handled manually by research assistances. 680 more words


How good is Columbus? A Bayesian approach

Columbus has been surprisingly good this year. As of this writing, the Blue Jackets are first in the league in points and goal differential with games in hand. 1,472 more words

Naive Bayes Classification explained with Python

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us). 318 more words


That hairy caterpillar

Textbooks on Bayesian inference often refer to a ‘hairy caterpillar’ when describing the traceplot and what it should look like. It’s easy to come across examples what things look like, examples of this hairy caterpillar: 135 more words

Didier Ruedin