Bayes, statistics, and reproducibility： “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.”
This is an abstract I wrote for a talk I didn’t end up giving. (The conference conflicted with something else I had to do that week.) But I thought it might interest some of you, so here it is:
Whats new on arXiv
Making Classification Competitive for Deep Metric Learning
Why Primary Research?
Deep Learning and Medical Image Analysis with Keras
GARCH and a rudimentary application to Vol Trading
Distilled News
Building a Random Forest from Scratch & Understanding Real-World Data Products (ML for Programmers – Part 3)
AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019
We have asked
The State of Data in Astronomy
Astronomy’s approach to data has drastically changed over the past two decades. From improved data collection methods to ML-based analytics, astronomers have more access to data than ever before.
Whats new on arXiv
Analyzing Federated Learning through an Adversarial Lens