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Title: Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library

Abstract

The use of Bayesian inference in data analysis has become the standard for large scienti c experiments [1, 2]. The Monte Carlo Codes Group(XCP-3) at Los Alamos has developed a simple set of algorithms currently implemented in C++ and Python to easily perform at-prior Markov Chain Monte Carlo Bayesian inference with pure Metropolis sampling. These implementations are designed to be user friendly and extensible for customization based on speci c application requirements. This document describes the algorithmic choices made and presents two use cases.

Authors:
 [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Programs (DP) (NA-10)
OSTI Identifier:
1417145
Report Number(s):
LA-UR-18-20185
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Bates, Cameron Russell, and Mckigney, Edward Allen. Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library. United States: N. p., 2018. Web. doi:10.2172/1417145.
Bates, Cameron Russell, & Mckigney, Edward Allen. Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library. United States. doi:10.2172/1417145.
Bates, Cameron Russell, and Mckigney, Edward Allen. Tue . "Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library". United States. doi:10.2172/1417145. https://www.osti.gov/servlets/purl/1417145.
@article{osti_1417145,
title = {Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library},
author = {Bates, Cameron Russell and Mckigney, Edward Allen},
abstractNote = {The use of Bayesian inference in data analysis has become the standard for large scienti c experiments [1, 2]. The Monte Carlo Codes Group(XCP-3) at Los Alamos has developed a simple set of algorithms currently implemented in C++ and Python to easily perform at-prior Markov Chain Monte Carlo Bayesian inference with pure Metropolis sampling. These implementations are designed to be user friendly and extensible for customization based on speci c application requirements. This document describes the algorithmic choices made and presents two use cases.},
doi = {10.2172/1417145},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 09 00:00:00 EST 2018},
month = {Tue Jan 09 00:00:00 EST 2018}
}

Technical Report:

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