Biparametric zero-modified power series distributions: Bayesian analysis under a reference prior approach

2017 ◽  
Vol 46 (21) ◽  
pp. 10507-10517
Author(s):  
Katiane S. Conceição ◽  
Vera Tomazella ◽  
Marinho G. Andrade ◽  
Francisco Louzada
Keyword(s):  
Power Series ◽  
Bayesian Analysis ◽  
Reference Prior ◽  
Power Series Distributions

scholarly journals Estimation of a Covariance Matrix Using the Reference Prior

1994 ◽  
Vol 22 (3) ◽  
pp. 1195-1211 ◽  
Author(s):  
Ruoyong Yang ◽  
James O. Berger
Keyword(s):  
Covariance Matrix ◽  
Reference Prior

Duplicate Publication in the Field of Otolaryngology-Head and Neck Surgery

2002 ◽  
Vol 126 (3) ◽  
pp. 211-216 ◽  
Author(s):  
Byron J. Bailey
Keyword(s):  
Head And Neck ◽  
Medical Literature ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Similar Data ◽  
Reference Prior ◽  
Duplication Rate ◽  
Duplicate Publication ◽  
Academic Conduct ◽  
Full Copy

OBJECTIVE: This study establishes the approximate prevalence and patterns of duplicate publication in the medical literature in the specialty of otolaryngology-head and neck surgery. STUDY DESIGN AND SETTING: All of the authors and articles published in the American Medical Association Archives of Otolaryngology-Head and Neck Surgery were identified and listed for an 8-year period. During this time, 1965 authors published 1082 articles in the Archives, and this same set of authors published a total of almost 50,000 articles during the 12-year period between January 1977 and December 1988. Of the same set of 1965 authors, we picked 1000 at random and found that they had published a total of 24,353 articles. The titles of these articles were then screened for similar titles, and when similarities were noted, the complete articles were obtained when possible and compared for the degree and pattern of duplicate publication. RESULTS: Of the 1000 authors studied, we found that 228 authors had published 938 articles with similar titles. We were able to obtain the full copy of 886 (94%) of the 938 articles in question, which were written by 226 (99%) of the 228 authors. We found that in the case of 25 authors, there was no duplication despite the similar titles, but in the case of 201 (20% of the 1000) authors, 644 articles were published with some degree of duplication (1.8% duplication rate). CONCLUSIONS: The most common duplicate publication involves sequential publication of very similar data and conclusions. Duplicate publications failed to reference prior articles by the same author 32% of the time or referenced the prior articles only partially (11% of the time). Artificial segmentation of a single study into multiple arbitrary segments composed 20% of the duplicate publication. Duplicate publication across different specialties was noted to account for 4% of the instances. Most of the authors duplicated only once or twice, and most duplicators do reference their prior publications. SIGNIFICANCE: Duplicate publication is an example of inappropriate academic conduct. Because it tarnishes the reputation of the duplicating author and represents an unfair practice in terms of displacing the work of others, efforts should continue to educate authors, particularly young academicians, to avoid the practice of duplicate publication.

scholarly journals A probabilistic geologic model of the Krafla geothermal system constrained by gravimetric data

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Samuel W. Scott ◽  
Cari Covell ◽  
Egill Júlíusson ◽  
Águst Valfells ◽  
Juliet Newson ◽  
...  
Keyword(s):  
Mass Density ◽  
Full Range ◽  
Probabilistic Constraints ◽  
Rock Properties ◽  
Geothermal System ◽  
Reference Prior ◽  
Geologic Structure ◽  
Prior Model ◽  
Gravimetric Data ◽  
Geologic Model

Abstract The quantitative connections between subsurface geologic structure and measured geophysical data allow 3D geologic models to be tested against measurements and geophysical anomalies to be interpreted in terms of geologic structure. Using a Bayesian framework, geophysical inversions are constrained by prior information in the form of a reference geologic model and probability density functions (pdfs) describing petrophysical properties of the different lithologic units. However, it is challenging to select the probabilistic weights and the structure of the prior model in such a way that the inversion process retains relevant geologic insights from the prior while also exploring the full range of plausible subsurface models. In this study, we investigate how the uncertainty of the prior (expressed using probabilistic constraints on commonality and shape) controls the inferred lithologic and mass density structure obtained by probabilistic inversion of gravimetric data measured at the Krafla geothermal system. We combine a reference prior geologic model with statistics for rock properties (grain density and porosity) in a Bayesian inference framework implemented in the GeoModeller software package. Posterior probability distributions for the inferred lithologic structure, mass density distribution, and uncertainty quantification metrics depend on the assumed geologic constraints and measurement error. As the uncertainty of the reference prior geologic model increases, the posterior lithologic structure deviates from the reference prior model in areas where it may be most likely to be inconsistent with the observed gravity data and may need to be revised. In Krafla, the strength of the gravity field reflects variations in the thickness of hyaloclastite and the depth to high-density basement intrusions. Moreover, the posterior results suggest that a WNW–ESE-oriented gravity low that transects the caldera may be associated with a zone of low hyaloclastite density. This study underscores the importance of reliable prior constraints on lithologic structure and rock properties during Bayesian geophysical inversion.

Reference analysis of non-regular models and nonparametric Bayes modeling of large data

2019 ◽  
Author(s):  
◽  
Chetkar Jha
Keyword(s):  
Bayesian Analysis ◽  
General Expression ◽  
General Model ◽  
Information Matrix ◽  
Small Sample ◽  
Reference Prior ◽  
Regular Case ◽  
Reference Priors ◽  
Regular Model ◽  
Continuous Parameter

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian analysis is a principled approach, which makes inference about the parameter, by combining the information gained from the data and the prior belief about the parameter. There's no convergence on the choice of priors, and often different motivations for prior lead to different areas of study in Bayesian statistics. This work is motivated by two such choices, namely: reference priors and nonparametric priors. Reference priors arise out of the need to specify priors in a non-subjective manner, i.e. objective manner. Reference priors maximize the amount of information gained from the data about the parameter, in information theoretical sense. The appeal of reference priors lies in the fact that it has nice frequentist properties even for small sample size and often avoids marginalization paradoxes in Bayesian analysis. However, reference prior algorithms are typically available when the posterior is asymptotically normal and Fisher's information matrix is well-defined. In statistical parlance, such models are called regular case or regular model. Recently, Berger et al. (2009) [1] proposed a general expression of reference prior for single continuous parameter model, which is applicable for both regular and non-regular case. Motivated by Berger et al. (2009) [1], we explore reference prior methodology for a general model. Specifically, we derive expression of reference prior for single continuous parameter truncated exponential family and a general expression of conditional reference prior for multi group continuous parameter model. Furthermore, we demonstrate the usefulness of our work by deriving reference priors for models which have no known existing reference priors. We also extend Datta and Ghosh (1996) [2]'s invariance result for reference prior of regular model to general model. Nonparametric priors arise out of the need to specify priors over a large support.

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