Copyright © 2017 University of Notre Dame

CSE Laboratory 372 Fitzpatrick Hall, Notre Dame, IN 46556, USA

Phone 574-631-2429   nzabaras@nd.edu

Contact us

Accessibility Information

Please reload

Recent Posts

Our new website is launched today.

March 14, 2017

1/1
Please reload

Featured Posts

Approximate Inference; Convolutional Neural Networks; Stein's Method

Approximate inference algorithms [Slides]

Souvik Chakraborty

 

 

Convolutional Neural Networks [Slides]

Navid Shervani-Tabar

 

 

Intro to Stein's method, with applications to Bayesian Surrogate Modeling [Slides]

Yinhao Zhu

 

Stein's method [1] is a theoretical technique to obtain bounds on the distances between probability distributions. It was recently combined with kernel methods and applied to Bayesian inference [2]. This talk reviews the basics of this recent development and shows an application to Bayesian neural net as a Bayesian surrogate for UQ [3].

 

References:

[1] https://sites.google.com/site/steinsmethod/

[2] Liu, Qiang, and Dilin Wang. "Stein variational gradient descent: A general purpose bayesian inference algorithm." Advances In Neural Information Processing Systems. 2016.

[3] Bilionis, Ilias, and Nicholas Zabaras. "Multi-output local Gaussian process regression: Applications to uncertainty quantification." Journal of Computational Physics 231.17 (2012): 5718-5746.

 

Tags:

Share on Twitter
Please reload

Please reload

Search By Tags
Please reload