University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Mixed moving average field guided learning for spatio-temporal data

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Mixed moving average field guided learning for spatio-temporal data

Download to your calendar using vCal

If you have a question about this talk, please contact nobody.

RCLW02 - Calibrating prediction uncertainty : statistics and machine learning perspectives

joint work with Lorenzo Proietti (TU Chemnitz) Influenced mixed moving average fields (MMAF) are a versatile modeling class for spatio-temporal data. However, their predictive distribution is not generally known. Under this modeling assumption, we define a novel spatio-temporal embedding and a theory-guided machine learning approach that employs a generalized Bayesian algorithm to make ensemble forecasts. Performing causal forecast is a highlight of our methodology as its potential application to data with temporal and spatial short and long-range dependence.    

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

11:30 am in THE LECTURE THEATRE, Institute for Manufacturing, Mill Lane Department of Engineering - Lecture Theatre 6 Average on Saturdays: 7614.2195122 kWh

Other talks

Afternoon Break There is no such thing as weather: a historical and cultural geography of weather (and health) Title to be confirmed Peri-Operative Medicine, and Obstetrics and Gynaecology Task performance predicts the substitution of feedforward by feedback drive in visual cortex LMB Seminar - Mechanisms of mRNA packaging and export
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity