A COMPARISON OF GRAPHICAL NOMOGRAM METHODS WITH A COMPUTERIZED BAYESIAN ANALYSIS METHOD IN THE INTERPRETATION OF SERUM PHENYTOIN CONCENTRATIONS

We develop a Bayesian analysis method for selecting the most probable equation of state under a set of constraints from compact star physics, which now include the tidal deformability from GW170817. We apply this method for the first time to a two-parameter family of hybrid equations of state that is based on realistic models for the hadronic phase (KVORcut02) and the quark matter phase (SFM α ) which produce a third family of hybrid stars in the mass–radius diagram. One parameter ( α ) characterizes the screening of the string tension in the string-flip model of quark matter while the other ( Δ P ) belongs to the mixed phase construction that mimics the thermodynamics of pasta phases and includes the Maxwell construction as a limiting case for Δ P = 0 . We present the corresponding results for compact star properties like mass, radius and tidal deformabilities and use empirical data for them in the newly developed Bayesian analysis method to obtain the probabilities for the model parameters within their considered range.

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A Simplified Bayesian Analysis Method for Vaccine Efficacy

10.1101/2020.12.07.20244954 ◽

2020 ◽

Author(s):

Carlo Graziani

Keyword(s):

Bayesian Analysis ◽

Vaccine Efficacy ◽

Vaccine Trial ◽

Nuisance Parameter ◽

Phase Iii ◽

Analysis Method ◽

Final Phase ◽

Posterior Density ◽

One Dimensional ◽

Probability Densities

AbstractWe describe a simplified Bayesian analysis of vaccine trial data, in which a reparametrization of the Poisson likelihood leads to a factorization in which the protective vaccine efficacy VES and the nuisance parameter appear in different factors. As a consequence the posterior density acquires a factorized form, and marginalization over the nuisance parameter is trivial. Estimates of VES accordingly become a matter of simple manipulations of one-dimensional posterior probability densities. We demonstrate the method using the publically-released data on the efficacy of three vaccines agains SARS-CoV-2: the final Phase III data from the Pfizer/BioNTech and Moderna mRNA vaccines and the interim data released for the Sputnik V adenovirus-based vaccine.

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Bayesian methods in palliative care research: cancer-induced bone pain

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2019-002160 ◽

2020 ◽

pp. bmjspcare-2019-002160

Author(s):

Richard A Parker ◽

Tonje A Sande ◽

Barry Laird ◽

Peter Hoskin ◽

Marie Fallon ◽

...

Keyword(s):

Bayesian Analysis ◽

Binary Data ◽

Bone Pain ◽

Bayesian Method ◽

Small Sample Size ◽

Small Sample ◽

Thermal Testing ◽

Analysis Method ◽

Predictive Values ◽

Recruitment And Retention

ObjectiveTo show how a simple Bayesian analysis method can be used to improve the evidence base in patient populations where recruitment and retention are challenging.MethodsA Bayesian conjugate analysis method was applied to binary data from the Thermal testing in Bone Pain (TiBoP) study: a prospective diagnostic accuracy/predictive study in patients with cancer-induced bone pain (CIBP). This study aimed to evaluate the clinical utility of a simple bedside tool to identify who was most likely to benefit from palliative radiotherapy (XRT) for CIBP.ResultsRecruitment and retention of patients were challenging due to the frail population, with only 27 patients available for the primary analysis. The Bayesian method allowed us to make use of prior work done in this area and combine it with the TiBoP data to maximise the informativeness of the results. Positive and negative predictive values were estimated with greater precision, and interpretation of results was facilitated by use of direct probability statements. In particular, there was only 7% probability that the true positive predictive value was above 80%.ConclusionsSeveral advantages of using Bayesian analysis are illustrated in this article. The Bayesian method allowed us to gain greater confidence in our interpretation of the results despite the small sample size by allowing us to incorporate data from a previous similar study. We suggest that this method is likely to be useful for the analysis of small diagnostic or predictive studies when prior information is available.

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Radio loudness and classification for radio sources

Publications of the Astronomical Society of Japan ◽

10.1093/pasj/psaa122 ◽

2021 ◽

Author(s):

Lixia Zhang ◽

Junhui Fan ◽

Jingtian Zhu

Keyword(s):

Bayesian Analysis ◽

Optical Emission ◽

Optical Data ◽

Radio Sources ◽

Analysis Method ◽

Radio Luminosity ◽

Classification Result ◽

R Value ◽

Positive Correlations ◽

Do So

Abstract The extragalactic radio sources are divided into two subclasses (radio-loud and radio-quiet sources) in the literature using radio loudness (R), which is defined as the ratio of radio emission to optical emission, but the boundary R-value separating the two classes is different in various sources. In this work, a sample of 2419 objects from the 13th catalog of quasars and active nuclei is used to build a boundary for the two subclasses. To do so, we compiled the radio and optical data, calculated their radio and optical indexes, made K-correction, obtained the radio loudness, and adopted a Bayesian analysis method to the logarithm of radio loudness for classification. We also investigated the correlations of radio loudness with radio/optical luminosities. Our main conclusions are summarized as follows: (1) The distribution of the logarithm of radio loudness (log R) is bimodal, the sources with log R < 1.26 are classified as radio-quiet sources, and those with log R > 1.26 are classified as radio-loud ones from the Bayesian analysis method. (2) The average radio-optical effective spectral index of radio-quiet sources is 〈αRO〉 = 0.05, while that of radio-loud sources is 〈αRO〉 = 0.55. (3) There are positive correlations between radio luminosity and radio loudness for both radio-loud sources and radio-quiet sources. (4) A dividing line of separating the distribution of the clusters on the diagram of radio loudness against radio luminosity was obtained statistically to set the boundary between radio-loud sources and radio-quiet sources, with an accuracy of $99.73\%$ based on the classification result from the Bayesian analysis method.

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An Application of the Extensive Bayesian Analysis Method to the Decision-Making of Disaster Prevention

2009 International Conference on Computational Intelligence and Software Engineering ◽

10.1109/cise.2009.5364242 ◽

2009 ◽

Author(s):

Shuai Hou ◽

Zhonggeng Han

Keyword(s):

Decision Making ◽

Bayesian Analysis ◽

Disaster Prevention ◽

Analysis Method

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Application of Bayesian Analysis Method in the Design Optimization of Permanent Casting Mold

Volume 3: Design, Materials and Manufacturing, Parts A, B, and C ◽

10.1115/imece2012-86413 ◽

2012 ◽

Author(s):

Yaou Wang ◽

David Schwam

Keyword(s):

Mechanical Properties ◽

Bayesian Analysis ◽

Design Optimization ◽

Casting Process ◽

Standard Test ◽

Shrinkage Porosity ◽

Analysis Method ◽

Operational Parameters ◽

Current Standard ◽

Metal Quality

This work is a case study of applying Bayesian analysis, a statistical data method, in the design optimization of permanent test-bar mold. The permanent test-bar mold is used in casting foundry to examine the metal quality. Since the current standard test-bar mold suffers from shrinkage porosity which detracts from best properties, a modified design is recently proposed to improve the mechanical properties. In order to validate the new design, Bayesian data analysis method is utilized to analyze the experimental data from the two designs. The effects of the mold designs and casting process operational parameters on the mechanical properties of castings are compared. Main effect to the mechanical properties is identified based on the Bayesian analysis.

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