scholarly journals BAT.jl Upgrading the Bayesian Analysis Toolkit

2020 ◽  
Vol 245 ◽  
pp. 06001
Author(s):  
Allen Caldwell ◽  
Cornelius Grunwald ◽  
Vasyl Hafych ◽  
Kevin Kröninger ◽  
Salvatore La Cagnina ◽  
...  
Keyword(s):  
Data Analysis ◽  
Bayesian Inference ◽  
Bayesian Analysis ◽  
Bayesian Statistics ◽  
Software Design ◽  
High Performance ◽  
Bayesian Reasoning ◽  
Modular Software ◽  
Performance Framework ◽  
Integration Algorithms

In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use of advanced algorithms. The Bayesian Analysis Toolkit (BAT) provides a collection of algorithms and methods that facilitate the application of Bayesian statistics to user-defined problems of arbitrary complexity. With BAT.jl, we present a modern rewrite of BAT in the Julia programming language. Through the use of a modular software design that is capable of running parallel and distributed, and by extending the tool with new sampling and integration algorithms, BAT.jl is a high-performance framework for Bayesian inference, meeting the requirements of modern data analysis.

scholarly journals Bayesian analysis in social sciences

2021 ◽  
Author(s):  
Minh-Hoang Nguyen
Keyword(s):  
Social Sciences ◽  
Data Analysis ◽  
Bayesian Inference ◽  
Bayesian Analysis ◽  
Bayesian Data Analysis ◽  
Replication Crisis ◽  
Current Article

Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural scientists are getting involved with Bayesian inference. Therefore, the current article provides a brief overview of programs (or software) and steps to conduct Bayesian data analysis in social sciences.

Understanding Bayesianism: Fundamentals for Process Tracers

2021 ◽  
pp. 1-8
Author(s):  
Andrew Bennett ◽  
Andrew E. Charman ◽  
Tasha Fairfield
Keyword(s):  
Social Science ◽  
Bayesian Analysis ◽  
Qualitative Methods ◽  
Recent Work ◽  
The State ◽  
Process Tracing ◽  
Bayesian Reasoning ◽  
Mutual Exclusivity ◽  
Positive View

Abstract Bayesian analysis has emerged as a rapidly expanding frontier in qualitative methods. Recent work in this journal has voiced various doubts regarding how to implement Bayesian process tracing and the costs versus benefits of this approach. In this response, we articulate a very different understanding of the state of the method and a much more positive view of what Bayesian reasoning can do to strengthen qualitative social science. Drawing on forthcoming research as well as our earlier work, we focus on clarifying issues involving mutual exclusivity of hypotheses, evidentiary import, adjudicating among more than two hypotheses, and the logic of iterative research, with the goal of elucidating how Bayesian analysis operates and pushing the field forward.

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