Data Science Austria

Markov chain Monte Carlo doesn’t “explore the posterior”

First some background, then the bad news, and finally the good news. Spoiler alert: The bad news is that exploring the posterior is intractable; the good news is that we don’t need to explore all of it. Sampling to characterize the posterior There’s a misconception among Markov chain Monte Carlo … Read moreMarkov chain Monte Carlo doesn’t “explore the posterior”

Using R and H2O to identify product anomalies during the manufacturing process.

Introduction: We will identify anomalous products on the production line by using measurements from testing stations and deep learning models. Anomalous products are not failures, these anomalies are products close to the measurement limits, so we can display warnings before the process starts to make failed products and in this … Read moreUsing R and H2O to identify product anomalies during the manufacturing process.

How cdata Control Table Data Transforms Work

With all of the excitement surrounding cdata style control table based data transforms (the cdata ideas being named as the “replacements” for tidyr‘s current methodology, by the tidyr authors themselves!) I thought I would take a moment to describe how they work. cdata defines two primary data manipulation operators: rowrecs_to_blocks() … Read moreHow cdata Control Table Data Transforms Work