Visualization in Bayesian workflow

Jonah Gabry; Daniel Simpson; Aki Vehtari; Michael Betancourt; Andrew Gelman

doi:10.1111/rssa.12378

Visualization in Bayesian workflow

Jonah Gabry^*, Daniel Simpson, Aki Vehtari, Michael Betancourt, Andrew Gelman

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

638 Citations (Scopus)

Abstract

Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

Original language	English
Pages (from-to)	389-402
Number of pages	14
Journal	Journal of the Royal Statistical Society. Series A: Statistics in Society
Volume	182
Issue number	2
DOIs	https://doi.org/10.1111/rssa.12378
Publication status	Published - 1 Feb 2019
MoE publication type	A1 Journal article-refereed

Keywords

Bayesian data analysis
Statistical graphics
Statistical workflow

Access to Document

10.1111/rssa.12378

https://arxiv.org/abs/1709.01449

Cite this

@article{04f33f2e6d5e4ef19974afc287f496db,

title = "Visualization in Bayesian workflow",

abstract = "Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.",

keywords = "Bayesian data analysis, Statistical graphics, Statistical workflow",

author = "Jonah Gabry and Daniel Simpson and Aki Vehtari and Michael Betancourt and Andrew Gelman",

year = "2019",

month = feb,

day = "1",

doi = "10.1111/rssa.12378",

language = "English",

volume = "182",

pages = "389--402",

journal = "Journal of the Royal Statistical Society. Series A: Statistics in Society",

issn = "0964-1998",

publisher = "Oxford University Press",

number = "2",

}

TY - JOUR

T1 - Visualization in Bayesian workflow

AU - Gabry, Jonah

AU - Simpson, Daniel

AU - Vehtari, Aki

AU - Betancourt, Michael

AU - Gelman, Andrew

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

AB - Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

KW - Bayesian data analysis

KW - Statistical graphics

KW - Statistical workflow

UR - http://www.scopus.com/inward/record.url?scp=85060108522&partnerID=8YFLogxK

U2 - 10.1111/rssa.12378

DO - 10.1111/rssa.12378

M3 - Article

AN - SCOPUS:85060108522

SN - 0964-1998

VL - 182

SP - 389

EP - 402

JO - Journal of the Royal Statistical Society. Series A: Statistics in Society

JF - Journal of the Royal Statistical Society. Series A: Statistics in Society

IS - 2

ER -

Visualization in Bayesian workflow

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this