Knowledge Completion System using Neuro-Symbolic-based Rule Induction and Inference Engine

2021 ◽  
Vol 48 (11) ◽  
pp. 1202-1210
Author(s):  
Won-Chul Shin ◽  
Hyun-Kyu Park ◽  
Young-Tack Park
Keyword(s):  
Inference Engine ◽  
Rule Induction

Corrigendum to WebPIE: A Web-Scale Parallel Inference Engine Using MapReduce

2012 ◽  
Author(s):  
Jacopo Urbani ◽  
Spyros Kotoulas ◽  
Jason Massen ◽  
Frank van Harmelen ◽  
Henri Bal
Keyword(s):  
Inference Engine ◽  
Parallel Inference

scholarly journals Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

2017 ◽  
Vol 17 (2) ◽  
pp. 330-342 ◽  
Author(s):  
Robert L. Grant ◽  
Bob Carpenter ◽  
Daniel C. Furr ◽  
Andrew Gelman
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Open Source ◽  
Item Response ◽  
Bayesian Modeling ◽  
Inference Engine ◽  
Software Components ◽  
Statistical Software ◽  
Simulation Based ◽  
Item Response Theory Models

In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

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