“Introduction to Bayesian statistics”
19 & 20 March 2019
London, UK
1 March 2019 (Barcelona, Spain) – Quite a few years ago I was invited to join the Royal Statistical Society (RSS) in London. It is where I had my first instruction on graphical interpretations of data: the good, the bad, the ugly. It is also the place where I have learned the most about machine learning. These sessions and events are run by “data wranglers”, people who know every facet of how to digest, to apply, and to test data relying on statistics.
It is also the organization that launched a guide for the legal profession on how to use basic statistics and probability which I wrote about two years ago. The RSS was the venue where I first learned about technology assisted review and predictive coding as applied in the e-discovery world, way before it hit the mainstream conference circuits at Legaltech and ILTA.
The RSS is a wonderful organization, set up as a charity that promotes statistics, data and evidence for the public good. It is a learned society and professional body, working with and for members to promote the role that statistics and data analysis play in society, share best practice, and support the profession.
They have a two-day course this month to provide a working knowledge of Bayesian statistics for interested researchers. Many statistical softwares are now available to professionals. However, these softwares were developed for statisticians and can often be daunting to non-statisticians. How do you know if you are pressing the right key, let alone performing the best test?
This particular RSS course is not going to be a “non-mathematical” introduction nor is it designed for non-statisticians. It will benefit professionals who are applied researchers. But there are so many of my subscribers that fit that category that I thought I would pass this on.
NOTE: later this year there will be a non-technical program for the legal profession on how to use basic statistics and probability in the law. I will keep you informed.
For details on the upcoming RSS Bayesian statistics program please click here.