scholarly journals TOWARDS A PROTOCOL FOR ADAPTIVE DYNAMICAL BAYESIAN INFERENCE: CASE OF LIMIT-CYCLE OSCILLATORS

2020 ◽  
Vol 5 (2) ◽  
pp. 85-92
Author(s):  
Duško Lukarski
Keyword(s):  
Bayesian Inference ◽  
Limit Cycle

Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations

2016 ◽  
Vol 316 ◽  
pp. 534-557 ◽  
Author(s):  
Rimple Sandhu ◽  
Dominique Poirel ◽  
Chris Pettit ◽  
Mohammad Khalil ◽  
Abhijit Sarkar
Keyword(s):  
Bayesian Inference ◽  
Limit Cycle ◽  
Unsteady Aerodynamics ◽  
Limit Cycle Oscillations

Optimal Limit-Cycle Control recast as Bayesian Inference

2011 ◽  
Vol 44 (1) ◽  
pp. 4707-4713 ◽  
Author(s):  
Yuval Tassa ◽  
Tom Erez ◽  
Emanuel Todorov
Keyword(s):  
Bayesian Inference ◽  
Limit Cycle

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.

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