Importance sampling: Intrinsic dimension and computational cost
S Agapiou, O Papaspiliopoulos, D Sanz-Alonso… - Statistical Science, 2017 - JSTOR
The basic idea of importance sampling is to use independent samples from a proposal
measure in order to approximate expectations with respect to a target measure. It is key to …
measure in order to approximate expectations with respect to a target measure. It is key to …
On the optimality of averaging in distributed statistical learning
JD Rosenblatt, B Nadler - Information and Inference: A Journal of …, 2016 - academic.oup.com
A common approach to statistical learning with Big-data is to randomly split it among
machines and learn the parameter of interest by averaging the individual estimates. In this …
machines and learn the parameter of interest by averaging the individual estimates. In this …
[PDF][PDF] Importance sampling: computational complexity and intrinsic dimension
S Agapiou, O Papaspiliopoulos… - arXiv preprint …, 2015 - authors.library.caltech.edu
The basic idea of importance sampling is to use independent samples from one measure in
order to approximate expectations with respect to another measure. Understanding how …
order to approximate expectations with respect to another measure. Understanding how …
Assimilating data into mathematical models
D Sanz-Alonso - 2016 - wrap.warwick.ac.uk
Chapter 1 is a brief overview of the Bayesian approach to blending mathematical models
with data. For this introductory chapter, I do not claim any originality in the material itself, but …
with data. For this introductory chapter, I do not claim any originality in the material itself, but …