scholarly journals Density estimation via Bayesian inference engines

2021 ◽  
Author(s):  
M. P. Wand ◽  
J. C. F. Yu
Keyword(s):  
Bayesian Inference ◽  
Density Estimation ◽  
Inference Engines

Bayesian Inference with Wavelets: Density Estimation

1998 ◽  
Vol 7 (4) ◽  
pp. 456 ◽  
Author(s):  
Peter Müller ◽  
Brani Vidakovic ◽  
Peter Muller
Keyword(s):  
Bayesian Inference ◽  
Density Estimation

Bayesian Inference with Wavelets: Density Estimation

1998 ◽  
Vol 7 (4) ◽  
pp. 456-468
Author(s):  
Peter Müller ◽  
Brani Vidakovic
Keyword(s):  
Bayesian Inference ◽  
Density Estimation

scholarly journals Bayesian Inference and Maximum Entropy Methods in Science and Engineering—MaxEnt 2019†

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 8
Author(s):  
Udo von Toussaint ◽  
Roland Preuss
Keyword(s):  
Parameter Estimation ◽  
Bayesian Inference ◽  
Data Processing ◽  
Maximum Entropy ◽  
Density Estimation ◽  
Model Comparison ◽  
Building Blocks ◽  
Research Area ◽  
Science And Engineering ◽  
Entropy Methods

As key building blocks for modern data processing and analysis methods—ranging from AI, ML and UQ to model comparison, density estimation and parameter estimation—Bayesian inference and entropic concepts are in the center of this rapidly growing research area. [...]

scholarly journals Higher-order probabilistic perceptrons as Bayesian inference engines

1999 ◽  
Vol 59 (5) ◽  
pp. 6161-6174 ◽  
Author(s):  
J. W. Clark ◽  
K. A. Gernoth ◽  
S. Dittmar ◽  
M. L. Ristig
Keyword(s):  
Bayesian Inference ◽  
Higher Order ◽  
Inference Engines

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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