An unsupervised Bayesian hierarchical method for medical fraud assessment

2019 ◽  
Vol 12 (2) ◽  
pp. 116-124 ◽  
Author(s):  
Tahir Ekin ◽  
Greg Lakomski ◽  
Rasim Muzaffer Musal
Keyword(s):  
Bayesian Hierarchical ◽  
Hierarchical Method ◽  
Bayesian Hierarchical Method

scholarly journals A Bayesian hierarchical method for multiple-event seismic location

2007 ◽  
Vol 171 (3) ◽  
pp. 1049-1063 ◽  
Author(s):  
Stephen C. Myers ◽  
Gardar Johannesson ◽  
William Hanley
Keyword(s):  
Bayesian Hierarchical ◽  
Hierarchical Method ◽  
Seismic Location ◽  
Bayesian Hierarchical Method

scholarly journals Bayesian hierarchical inference of asteroseismic inclination angles

2019 ◽  
Vol 488 (1) ◽  
pp. 572-589 ◽  
Author(s):  
James S Kuszlewicz ◽  
William J Chaplin ◽  
Thomas S H North ◽  
Will M Farr ◽  
Keaton J Bell ◽  
...  
Keyword(s):  
Inclination Angle ◽  
Angle Distribution ◽  
Data Set ◽  
Single Star ◽  
Bayesian Hierarchical ◽  
Hierarchical Method ◽  
Giant Stars ◽  
Inclination Angles ◽  
Red Giant Stars ◽  
Red Giant

Abstract The stellar inclination angle – the angle between the rotation axis of a star and our line of sight – provides valuable information in many different areas, from the characterization of the geometry of exoplanetary and eclipsing binary systems to the formation and evolution of those systems. We propose a method based on asteroseismology and a Bayesian hierarchical scheme for extracting the inclination angle of a single star. This hierarchical method therefore provides a means to both accurately and robustly extract inclination angles from red giant stars. We successfully apply this technique to an artificial data set with an underlying isotropic inclination angle distribution to verify the method. We also apply this technique to 123 red giant stars observed with Kepler. We also show the need for a selection function to account for possible population-level biases, which are not present in individual star-by-star cases, in order to extend the hierarchical method towards inferring underlying population inclination angle distributions.

scholarly journals Filling in knowledge gaps: Length–weight relations of 46 uncommon sharks and rays (Elasmobranchii) in the Mediterranean Sea

2021 ◽  
Vol 51 (3) ◽  
pp. 249-255
Author(s):  
Athanassios C. Tsikliras ◽  
Donna Dimarchopoulou
Keyword(s):  
Mediterranean Sea ◽  
Eastern Mediterranean ◽  
Global Scale ◽  
Biological Information ◽  
Knowledge Gap ◽  
Museum Collections ◽  
Bayesian Hierarchical ◽  
The Mediterranean ◽  
Species Specific ◽  
Bayesian Hierarchical Method

Large sharks and rays are generally understudied in the Mediterranean Sea, thus leading to a knowledge gap of basic biological characteristics that are important in fisheries management and ecosystem modeling. Out of the 76 sharks and rays inhabiting the Mediterranean Sea, the length–weight relations (LWR) are available for 28 (37%) of them, usually for common small-sized species that are not protected and may be marketed. The aim of the presently reported study was to fill in the knowledge gap through the estimation of LWR of rare and uncommon sharks and rays in the Mediterranean Sea using the information from single records or few individuals. The analysis was based on a Bayesian hierarchical method for estimating length–weight relations in fishes that has been recently proposed for data-deficient species or museum collections and uses the prior knowledge and existing LWR studies to derive species-specific LWR parameters by body form. The use of this method was applied to single records of rare and uncommon species and here we report the LWR of 46 uncommon sharks and ray species, 14 of which are first reported LWR at a global scale and 21 are the first reported LWR for the Mediterranean Sea; the remaining 11 species are first time records for the western or eastern Mediterranean regions. Museum collections and sporadic catch records of rare emblematic species may provide useful biological information with the use of appropriate Bayesian methods.

scholarly journals Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

2021 ◽  
Vol 3 ◽  
Author(s):  
Dimitrios Kiagias ◽  
Giulia Russo ◽  
Giuseppe Sgroi ◽  
Francesco Pappalardo ◽  
Miguel A. Juárez
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Posterior Distribution ◽  
In Silico ◽  
Therapeutic Vaccination ◽  
Virtual Patients ◽  
Bayesian Hierarchical ◽  
Bayesian Hierarchical Method ◽  
Joint Posterior Distribution

We propose a Bayesian hierarchical method for combining in silico and in vivo data onto an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the in vivo data. We also formalise the contribution and impact of in silico information in the augmented trial. We illustrate our approach to inference with in silico data from the UISS-TB simulator, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.

scholarly journals A Spatiotemporal Prediction Model for Regional Scheduling of Shared Bicycles Based on the INLA Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhuoran Yu ◽  
Yimeng Duan ◽  
Shen Zhang ◽  
Xin Liu ◽  
Kui Li
Keyword(s):  
Prediction Models ◽  
Bayesian Hierarchical ◽  
Demand Prediction ◽  
Point Of Interest ◽  
Modeling Process ◽  
Proposed Model ◽  
Bayesian Hierarchical Method ◽  
Spatial Entities ◽  
Operation Results ◽  
The Impact

Dock-less bicycle-sharing programs have been widely accepted as an efficient mode to benefit health and reduce congestions. And modeling and prediction has always been a core proposition in the field of transportation. Most of the existing demand prediction models for shared bikes take regions as research objects; therefore, a POI-based method can be a beneficial complement to existing research, including zone-level, OD-level, and station-level techniques. Point of interest (POI) is the location description of spatial entities, which can reflect the cycling route characteristics for both commuting and noncommuting trips to a certain extent, and is also the main generating point and attraction point of shared-bike travel flow. In this study, we make an effort to model a POI-level cycling demand with a Bayesian hierarchical method. The proposed model combines the integrated nested Laplace approximation (INLA) and random partial differential equation (SPDE) to cope with the huge computation in the modeling process. In particular, we have adopted the dock-less bicycle-sharing rental records of Mobike as a case study to validate our method; the study area was one of the fastest growing urban districts in Shanghai in August 2016. The operation results show that the method can help better understand, measure, and characterize spatiotemporal patterns of bike-share ridership at the POI level and quantify the impact of the spatiotemporal effect on bicycle-sharing use.

scholarly journals Filling in knowledge gaps: Length–weight relations of 46 uncommon sharks and rays (Elasmobranchii) in the Mediterranean Sea

2021 ◽  
Vol 51 (3) ◽  
pp. 249-255
Author(s):  
Athanassios C. Tsikliras ◽  
Donna Dimarchopoulou
Keyword(s):  
Mediterranean Sea ◽  
Eastern Mediterranean ◽  
Global Scale ◽  
Biological Information ◽  
Knowledge Gap ◽  
Museum Collections ◽  
Bayesian Hierarchical ◽  
The Mediterranean ◽  
Species Specific ◽  
Bayesian Hierarchical Method

Large sharks and rays are generally understudied in the Mediterranean Sea, thus leading to a knowledge gap of basic biological characteristics that are important in fisheries management and ecosystem modeling. Out of the 76 sharks and rays inhabiting the Mediterranean Sea, the length–weight relations (LWR) are available for 28 (37%) of them, usually for common small-sized species that are not protected and may be marketed. The aim of the presently reported study was to fill in the knowledge gap through the estimation of LWR of rare and uncommon sharks and rays in the Mediterranean Sea using the information from single records or few individuals. The analysis was based on a Bayesian hierarchical method for estimating length–weight relations in fishes that has been recently proposed for data-deficient species or museum collections and uses the prior knowledge and existing LWR studies to derive species-specific LWR parameters by body form. The use of this method was applied to single records of rare and uncommon species and here we report the LWR of 46 uncommon sharks and ray species, 14 of which are first reported LWR at a global scale and 21 are the first reported LWR for the Mediterranean Sea; the remaining 11 species are first time records for the western or eastern Mediterranean regions. Museum collections and sporadic catch records of rare emblematic species may provide useful biological information with the use of appropriate Bayesian methods.

scholarly journals Melissa: Bayesian clustering and imputation of single cell methylomes

2018 ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Guido Sanguinetti
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Real Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Control Of Gene Expression ◽  
Bayesian Hierarchical ◽  
Cpg Sites ◽  
Bisulfite Sequencing Data ◽  
Bayesian Hierarchical Method

AbstractMeasurements of DNA methylation at the single cell level are promising to revolutionise our understanding of epigenetic control of gene expression. Yet, intrinsic limitations of the technology result in very sparse coverage of CpG sites (around 5% to 20% coverage), effectively limiting the analysis repertoire to a semi-quantitative level. Here we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to quantify spatially-varying methylation profiles across genomic regions from single-cell bisulfite sequencing data (scBS-seq). Melissa clusters individual cells based on local methylation patterns, enabling the discovery of epigenetic differences and similarities among individual cells. The clustering also acts as an effective regularisation method for imputation of methylation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings, and state-of-the-art imputation performance. An R implementation of Melissa is publicly available at https://github.com/andreaskapou/Melissa.

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