Quants see promise in Bayesian machine learning

Risk USA: probability theory may hold key to creating ‘self-aware’ AI

Robot artificial intelligence

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 18th 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 info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

The changing shape of risk

S&P Global Market Intelligence’s head of credit and risk solutions reveals how firms are adjusting their strategies and capabilities to embrace a more holistic view of risk

Most read articles loading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here