scholarly journals The Parallel C++ Statistical Library for Bayesian Inference: QUESO

2015 ◽  
pp. 1-38
Author(s):  
Damon McDougall ◽  
Nicholas Malaya ◽  
Robert D. Moser
Keyword(s):  
Bayesian Inference ◽  
Statistical Library

scholarly journals The Parallel C++ Statistical Library for Bayesian Inference: QUESO

2017 ◽  
pp. 1829-1865
Author(s):  
Damon McDougall ◽  
Nicholas Malaya ◽  
Robert D. Moser
Keyword(s):  
Bayesian Inference ◽  
Statistical Library

Fundamentals of Nonparametric Bayesian Inference

2017 ◽  
Author(s):  
Subhashis Ghosal ◽  
Aad van der Vaart
Keyword(s):  
Bayesian Inference ◽  
Nonparametric Bayesian

Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

How do academics assess the results of multiple experiments?

2018 ◽  
Author(s):  
Olmo Van den Akker ◽  
Linda Dominguez Alvarez ◽  
Marjan Bakker ◽  
Jelte M. Wicherts ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Bayesian Inference ◽  
Secondary Analysis ◽  
Individual Participant Data ◽  
Additional Experiment ◽  
Underlying Theory ◽  
Individual Participant ◽  
Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

Bayesian Inference of nrDNA ITS Sequences from Machilus (Lauraceae) and Its Systematic Significance

2009 ◽  
Vol 31 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Jun-Qiu CHEN ◽  
Lang LI ◽  
Jie LI ◽  
Hsi-Wen LI
Keyword(s):  
Bayesian Inference ◽  
Its Sequences ◽  
Nrdna Its ◽  
Systematic Significance
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