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The Bayes Inference Engine (BIE) is a general software tool intended to be used primarily in the analysis of radiographic data for density. An analysis is set up in the BIE by representing the problem as a collection of modules called glyphs. All glyphs to be used in creating the forward model of the experiment will be tested separately for a range of inputs.
The Bayes Inference Engine (BIE) is a general software tool intended to be used primarily in the analysis of radiographic data for density. An analysis is set up in the BIE by representing the problem as a collection of modules called glyphs. All glyphs to be used in creating the forward model of the experiment will be tested separately for a range of inputs.
The Bayes Inference Engine (BIE) is a general software tool intended to be used primarily in the analysis of radiographic data for density. An analysis is set up in the BIE by representing the problem as a collection of modules called glyphs. All glyphs to be used in creating the forward model of the experiment will be tested separately for a range of inputs. The testing of the adjoint code is performed in a separate document.
The Bayes Inference Engine (BIE) is a general software tool intended to be used primarily in the analysis of radiographic data for density. An analysis is set up in the BIE by representing the problem as a collection of modules called glyphs. All glyphs to be used in creating the forward model of the experiment will be tested separately for a range of inputs. The testing of the adjoint code is performed in a separate document.
A modified radiographer’s equation is derived and fitted to simulation data so as to describe the increase in radiographic signal that comes with increasing bremsstrahlung spectrum endpoint energy. The dose contained in the incident spectrum follows an E28 power law, as often cited. However, the direct flux penetrating through a heavy, high-Z material, obeys a weaker scaling due to hardening of the radiation. The resultant expression depends on the thickness of the scene being radiographed, the composition of high-Z materials in the scene, and any filtering by other low-Z materials, such as Al or Be. The expression exhibits two asymptotic limits: a ‘thick’ limit at which radiation is extremely hardened and only a weak benefit to increasing endpoint energy is observed (also the most attenuating limit and therefore of dubious physical significance), and a ‘thin’ limit at which a minimum of high-Z material is present and the penetrating dose exhibits the 2.8 power scaling inherent in the incident spectrum. Behavior for intermediate thicknesses of W and Al are obtained by modeling incident and attenuated dose using the DOSECALC code and the Bayes’ Inference Engine (BIE). These data guide the optimization of MeV radiography over a wide range of energies where pair production plays a significant role in photon scattering.
SAND2024-09052O Quasi-Newton Variational Bayes (QNVB) is a training algorithm that performs high-dimensional variational inference on machine learning models. It provides mechanisms to calibrate and control model uncertainty during training. The PyTorch implementation of projective integral updates for Gaussian mean-fields supports the manuscript, "Projective Integral Updates for High-Dimensional Variational Inference." Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results—especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes’ rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated ‘engine’ capable of computationally executing analysis decisions currently made by visual inspection across the sciences.