scholarly journals Bayesian inference for radio observations

2015 ◽  
Vol 450 (2) ◽  
pp. 1308-1319 ◽  
Author(s):  
Michelle Lochner ◽  
Iniyan Natarajan ◽  
Jonathan T. L. Zwart ◽  
Oleg Smirnov ◽  
Bruce A. Bassett ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Radio Observations

scholarly journals Montblanc : GPU accelerated radio interferometer measurement equations in support of Bayesian inference for radio observations

2015 ◽  
Vol 12 ◽  
pp. 73-85 ◽  
Author(s):  
S.J. Perkins ◽  
P.C. Marais ◽  
J.T.L. Zwart ◽  
I. Natarajan ◽  
C. Tasse ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Radio Interferometer ◽  
Radio Observations ◽  
Measurement Equations

scholarly journals Bayesian Inference for Radio Observations - Going beyond deconvolution

2014 ◽  
Vol 10 (S306) ◽  
pp. 185-188
Author(s):  
Michelle Lochner ◽  
Bruce Bassett ◽  
Martin Kunz ◽  
Iniyan Natarajan ◽  
Nadeem Oozeer ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Radio Telescope ◽  
Alternative Technique ◽  
Instrumental Effects ◽  
Missing Information ◽  
Radio Observations ◽  
Pointing Errors ◽  
Statistical Framework ◽  
Simple Simulation ◽  
Beam Parameters

AbstractRadio interferometers suffer from the problem of missing information in their data, due to the gaps between the antennae. This results in artifacts, such as bright rings around sources, in the images obtained. Multiple deconvolution algorithms have been proposed to solve this problem and produce cleaner radio images. However, these algorithms are unable to correctly estimate uncertainties in derived scientific parameters or to always include the effects of instrumental errors. We propose an alternative technique called Bayesian Inference for Radio Observations (BIRO) which uses a Bayesian statistical framework to determine the scientific parameters and instrumental errors simultaneously directly from the raw data, without making an image. We use a simple simulation of Westerbork Synthesis Radio Telescope data including pointing errors and beam parameters as instrumental effects, to demonstrate the use of BIRO.

Low frequency radio observations: critical in revealing inhomogeneous shock structures

2019 ◽  
Author(s):  
Poonam Chandra ◽  
A. J. Nayana ◽  
Claes-Ingvar Bjornsson ◽  
Peter Lundqvist ◽  
Alak K. Ray
Keyword(s):  
Low Frequency ◽  
Radio Observations

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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