- The Bayesian Prior

2014 ◽  
pp. 142-189
Keyword(s):  
Bayesian Prior

scholarly journals Retract p < 0.005 and propose using JASP, instead

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 2122 ◽  
Author(s):  
Jose D. Perezgonzalez ◽  
M. Dolores Frías-Navarro
Keyword(s):  
Statistical Significance ◽  
Life Sciences ◽  
Large Mass ◽  
Bayes Factors ◽  
Error Statistics ◽  
Factors Analysis ◽  
Original Argument ◽  
Potential Impact ◽  
Bayesian Prior

Seeking to address the lack of research reproducibility in science, including psychology and the life sciences, a pragmatic solution has been raised recently:  to use a stricter p < 0.005 standard for statistical significance when claiming evidence of new discoveries. Notwithstanding its potential impact, the proposal has motivated a large mass of authors to dispute it from different philosophical and methodological angles. This article reflects on the original argument and the consequent counterarguments, and concludes with a simpler and better-suited alternative that the authors of the proposal knew about and, perhaps, should have made from their Jeffresian perspective: to use a Bayes factors analysis in parallel (e.g., via JASP) in order to learn more about frequentist error statistics and about Bayesian prior and posterior beliefs without having to mix inconsistent research philosophies.

scholarly journals Rapid Deconvolution of Low-Resolution Time-of-Flight Data using Bayesian Inference

2019 ◽  
Author(s):  
Cornelius Pieterse ◽  
Michiel B. De Kock ◽  
Wesley D. Robertson ◽  
Hans C. Eggers ◽  
R. J. Dwayne Miller
Keyword(s):  
Time Of Flight ◽  
Stopping Criterion ◽  
Amplitude Ratios ◽  
Resolution Time ◽  
Low Resolution ◽  
Flight Data ◽  
Additional Information ◽  
Bayesian Prior ◽  
Accurate Quantification ◽  
General Method

Deconvolution of low-resolution time-of-flight data has numerous advantages including the ability to extract additional information from the experimental data. We augment the well-known Lucy-Richardson deconvolution algorithm by various Bayesian prior distributions and show that a prior of second-differences of the signal outperforms the standard Lucy-Richardson algorithm, accelerating the rate of convergence by more than a factor of four, while preserving the peak amplitude ratios of a similar fraction of the total peaks. A novel stopping criterion and boosting mechanism is implemented to ensure these methods converge to a similar entropy, and that local minima are avoided, respectively. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper by the deconvolution of fragmentation peaks of the DHB matrix, as well as the BTP thermometer ion, following femtosecond ultraviolet laser desorption.

scholarly journals The Role of Frontal Cortical and Medial-Temporal Lobe Brain Areas in Learning a Bayesian Prior Belief on Reversals

2015 ◽  
Vol 35 (33) ◽  
pp. 11751-11760 ◽  
Author(s):  
Anthony I. Jang ◽  
Vincent D. Costa ◽  
Peter H. Rudebeck ◽  
Yogita Chudasama ◽  
Elisabeth A. Murray ◽  
...  
Keyword(s):  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Prior Belief ◽  
Brain Areas ◽  
Bayesian Prior

Modeling Customer-Related IT Diffusion

2011 ◽  
pp. 251-263
Author(s):  
Shana L. Dardan ◽  
Ram L. Kumar ◽  
Antonis C. Stylianou
Keyword(s):  
Information Technology ◽  
Data Collection ◽  
Diffusion Model ◽  
Technology Diffusion ◽  
Empirical Support ◽  
Event Studies ◽  
It Investments ◽  
It Diffusion ◽  
Information Technology Investments ◽  
Bayesian Prior

This study develops a diffusion model of customer-related IT (CRIT) based on stock market announcements of investments in those technologies. Customer-related IT investments are defined in this work as information technology investments made with the intention of improving or enhancing the customer experience. The diffusion model developed in our study is based on data for the companies of the S&P 500 and S&P MidCap 400 for the years of 1996-2001. We find empirical support for a sigmoid diffusion model. Further, we find that both the size and industry of the company affect the path of CRIT diffusion. Another contribution of this study is to illustrate how data collection techniques typically used for fi- nancial event studies can be used to study information technology diffusion. Finally, the data collected for this study can serve as a Bayesian prior for future diffusion forecasting studies of CRIT.

Classical error pairs and the Bayesian prior

1973 ◽  
Vol 1 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Buddy L. Myers ◽  
N.L Enrick
Keyword(s):  
Bayesian Prior

scholarly journals Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior

2019 ◽  
Vol 16 (150) ◽  
pp. 20180572 ◽  
Author(s):  
W. Thomson ◽  
S. Jabbari ◽  
A. E. Taylor ◽  
W. Arlt ◽  
D. J. Smith
Keyword(s):  
Parameter Estimation ◽  
Statistical Models ◽  
Prior Distribution ◽  
Biological Data ◽  
Variable Model ◽  
Machine Learning Methods ◽  
Spike And Slab Prior ◽  
Unseen Data ◽  
Continuous Analogue ◽  
Bayesian Prior

We introduce a Bayesian prior distribution, the logit-normal continuous analogue of the spike-and-slab, which enables flexible parameter estimation and variable/model selection in a variety of settings. We demonstrate its use and efficacy in three case studies—a simulation study and two studies on real biological data from the fields of metabolomics and genomics. The prior allows the use of classical statistical models, which are easily interpretable and well known to applied scientists, but performs comparably to common machine learning methods in terms of generalizability to previously unseen data.

scholarly journals Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees

PLoS ONE ◽  
2008 ◽  
Vol 3 (7) ◽  
pp. e2651 ◽  
Author(s):  
Leonardo de Oliveira Martins ◽  
Élcio Leal ◽  
Hirohisa Kishino
Keyword(s):  
Bayesian Prior
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