Markov chain Monte Carlo algorithms for optimizing grazing incidence optics for wide-field x-ray survey imaging

Peter W.A. Roming; John C. Liechty; David H. Sohn; Jared R. Shoemaker; David N. Burrows; Gordon P. Garmire

doi:10.1117/12.454361

Markov chain Monte Carlo algorithms for optimizing grazing incidence optics for wide-field x-ray survey imaging

Peter W.A. Roming, John C. Liechty, David H. Sohn, Jared R. Shoemaker, David N. Burrows, Gordon P. Garmire

Research output: Contribution to journal › Conference article › peer-review

8 Scopus citations

Abstract

The Markov chain Monte Carlo (MCMC) algorithms as a method for optimizing the multi-dimensional coefficient space were investigated. Although MCMC algorithms were traditionally used to explore probability densities that result from a particular model specification, they can be used to create irreducible algorithms for optimizing arbitrary, bounded functions. The irreducible nature of the MCMC algorithm combined with the ability to adapt MCMC algorithms offers a promising framework for optimizing the multi-dimensional complex function.

Original language	English (US)
Pages (from-to)	146-153
Number of pages	8
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4496
DOIs	https://doi.org/10.1117/12.454361
State	Published - 2002
Event	X-Ray Optics for Astronomy: Telescopes, Multilayers, Spectrometers, and Missions - San Diego, CA, United States Duration: Jul 30 2001 → Jul 30 2001

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.454361

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abstract = "The Markov chain Monte Carlo (MCMC) algorithms as a method for optimizing the multi-dimensional coefficient space were investigated. Although MCMC algorithms were traditionally used to explore probability densities that result from a particular model specification, they can be used to create irreducible algorithms for optimizing arbitrary, bounded functions. The irreducible nature of the MCMC algorithm combined with the ability to adapt MCMC algorithms offers a promising framework for optimizing the multi-dimensional complex function.",

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AU - Roming, Peter W.A.

AU - Liechty, John C.

AU - Sohn, David H.

AU - Shoemaker, Jared R.

AU - Burrows, David N.

AU - Garmire, Gordon P.

PY - 2002

Y1 - 2002

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AB - The Markov chain Monte Carlo (MCMC) algorithms as a method for optimizing the multi-dimensional coefficient space were investigated. Although MCMC algorithms were traditionally used to explore probability densities that result from a particular model specification, they can be used to create irreducible algorithms for optimizing arbitrary, bounded functions. The irreducible nature of the MCMC algorithm combined with the ability to adapt MCMC algorithms offers a promising framework for optimizing the multi-dimensional complex function.

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DO - 10.1117/12.454361

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JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - X-Ray Optics for Astronomy: Telescopes, Multilayers, Spectrometers, and Missions

Y2 - 30 July 2001 through 30 July 2001

ER -

Markov chain Monte Carlo algorithms for optimizing grazing incidence optics for wide-field x-ray survey imaging

Abstract

All Science Journal Classification (ASJC) codes

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