scholarly journals Bayesian Approach to Estimating Fireball Parameters From Remote Sensing Data

2019 ◽  
Author(s):  
Derek E. Armstrong
Keyword(s):  
Bayesian Model Averaging ◽  
Thermal Emission ◽  
Optimal Parameter ◽  
Model Averaging ◽  
Remote Sensing Data ◽  
Infrared Region ◽  
Optical Signal ◽  
Bayesian Approaches ◽  
Particulate Emission ◽  
Inverse Optimization Problem

Abstract Remote sensors in the infrared region can be used to study the progression of fireballs generated from experiments involving high explosives (HE). Developing an improved understanding of HE fireballs can be used to validate and improve computational physics codes that simulate such events. In this paper, Bayesian approaches are studied to estimate time-dependent optimal fireball parameters and their uncertainties using Fourier transform infrared (FTIR) spectroscopy. The optical signal measured by an FTIR sensor provides information on the fireball due to thermal emission, particulate emission/absorption, and HE gas product emission/absorption from the fireball. FTIR sensors have the advantage of being able to capture and measure the radiance in a large part of the infrared spectrum. The parameters to be estimated from the fireball include temperature and size, soot quantity, gas species concentrations (e.g., H2O, CO2, CO), and information on the presence of metals. In general, this inverse optimization problem is difficult due to the estimated quantities being correlated, the low spectral resolution of the FTIR sensor, and the intervening atmosphere absorbing the radiation emitted from the fireball. Bayesian calibration and Bayesian model averaging are applied to address these difficulties and to quantify the uncertainty in the estimated optimal parameter values. The fireball parameter settings are evaluated by the fit of a simplified spectral model to FTIR data. The overall problem will be presented together with a description of the Bayesian approaches. In this paper, the Bayesian approaches are applied to artificially generated FTIR data to illustrate the approach.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

Exploring the drivers of wildlife population dynamics from insufficient data by Bayesian model averaging

2015 ◽  
Vol 57 (3) ◽  
pp. 485-493 ◽  
Author(s):  
Yutaka Osada ◽  
Takeo Kuriyama ◽  
Masahiko Asada ◽  
Hiroyuki Yokomizo ◽  
Tadashi Miyashita
Keyword(s):  
Population Dynamics ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Insufficient Data ◽  
Wildlife Population
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