# 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:

- 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}

}

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