Quants see promise in Bayesian machine learning

Tweet
Facebook
LinkedIn
Save this article
Send to
Print this page

A 250-year-old mathematical theory could be used to create ‘self-aware’ machine learning systems that understand when they are out of their depth, according to a panel of senior quants.

Bayes’ theorem, named after the 18^th century UK mathematician Thomas Bayes, is widely used to infer the probability of a hypothesis holding true as more information becomes available.

“The Bayesian paradigm allows you to actually get a hint that maybe the data the model has been trained on is not relevant

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to print this content. Please contact [email protected] to find out more.

You are currently unable to copy this content. Please contact [email protected] to find out more.

You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/

If you would like to purchase additional rights please email [email protected]

You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/

If you would like to purchase additional rights please email [email protected]