Inference for a proton accelerator using convolution models

doi:10.1198/016214507000000833

Inference for a proton accelerator using convolution models

2012-01-01

5

PRE

AI

DOI：10.1198/016214507000000833

原文链接

原文求助

摘要

En 中文

Proton beams present difficulties in analysis because of the limited data that can be collected. The study of such beams must depend on complex computer simulators that incorporate detailed physical equations. The statistical problem of interest is to infer the initial state of the beam from the limited data collected as the beam passes through a series of focusing magnets. We are thus faced with a classic inverse problem where the computer simulator links the initial state to the observables. We propose a new model for the initial distribution that is derived from the discretized process convolution approach. This model provides a computationally tractable method for this highly challenging problem. Taking a Bayesian perspective allows better estimation of the uncertainty and propagation of this uncertainty.

Keyword:

Bayesian statistics

computer simulator

density estimation

inverse problem

Inference for a proton accelerator using convolution models

摘要

AI总结

期刊

机构

引用论文