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"Augmenting 3D Ultrasound Strain Elastography by combining Bayesian inf" by Shuojie Wen, Bo Peng et al.

Accurately tracking large tissue motion over a sequence of ultrasound images is critically important to several clinical applications including, but not limited to, elastography, flow imaging, and ultrasound-guided motion compensation. However, tracking in vivo large tissue deformation in 3D is a challenging problem and requires further developments. In this study, we explore a novel tracking strategy that combines Bayesian inference with local polynomial fitting. Since this strategy is incorporated into a region-growing block-matching motion tracking framework we call this strategy a Bayesian region-growing motion tracking with local polynomial fitting (BRGMT-LPF) algorithm. More specifically, unlike a conventional block-matching algorithm, we use a maximum posterior probability density function to determine the “correct” three-dimensional displacement vector. The proposed BRGMT-LPF algorithm was evaluated using a tissue-mimicking phantom and ultrasound data acquired from a pathol ....

3d Motion Tracking , Bayesian Inference , Breast Imaging , Ltrasound Strain Elastography ,

"Bayesian analysis of De distributions in optical dating: Towards a rob" by Bo Li, Zenobia Jacobs et al.

In optical dating, especially single-grain dating, various patterns of distributions in equivalent dose (De) are usually observed and analysed using different statistical models. None of these methods, however, is designed to deal with outliers that do not form part of the population of grains associated with the event of interest (the ‘target population’), despite outliers being commonly present in single-grain De distributions. In this paper, we present a Bayesian method for detecting De outliers and making allowance for them when estimating the De value of the target population. We test this so-called Bayesian outlier model (BOM) using data sets obtained for individual grains of quartz from sediments deposited in a variety of settings, and in simulations. We find that the BOM is suitable for single-grain De distributions containing outliers that, for a variety of reasons, do not form part of the target population. For example, De outliers may be associated with grains that have ....

Page Models , Bayesian Inference , Equivalent Doses , Outlier Detection , Single Grains ,

talks.cam : Nornalizing Flows for cosmology applications

talks.cam : Nornalizing Flows for cosmology applications
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Uros Seljak Berkeley , James Bonifacio , Nornalizing Flows , Sliced Iterative , Optimal Transport , Bayesian Inference , Global Optimization , Monte Carlo Markov , Normalizing Flow , Translational Equivariance , Cosmology Lunch , University Of Cambridge ,

Fly For Your Life: An Assessment Of Medical Helicopter Risks

Fly For Your Life: An Assessment Of Medical Helicopter Risks
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New York , United States , Israel General , Joseph Keebler , Alex Chaparro , Richard Simonson , Volusia Sheriff Office , Air One , National Transportation Safety Board , Riddle Aeronautical University , Embry Riddle Aeronautical University , Aviation Medicine , Human Performance , Instrument Flight Rules , Volusia Sheriff , Special Operations Section Aviation Unit , Bayesian Inference ,

How can Bayesien Inference support complex decisions? A practical guide to an overlooked approach

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Summary:
For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here s a practical guide to how Bayes Nets can solve enterprise problems.
In part one of this series, we covered some basic probability theory principles - and compared Machine Learning approaches to Bayesian Belief Nets (Can Bayesian Networks provide answers when Machine Learning comes up short?). In this article, we ll dig a little deeper into Bayesian Belief Networks and how they can be applied to complex decisions.
Understanding Bayesian Inference
In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, very few have any experience implementing Judea Pearl s Bayesian Belief Networks: ....

Alison Gopnik , Thomas Bayes , Jennifer Lawrence , Alison Glopnik , Judea Pearl Bayesian Belief Networks , Bayesian Belief Nets Can Networks , Bayesian Network , Bayesian Networks , Bayesian Belief Networks , Machine Learning , Bayesian Belief Nets , Can Bayesian Networks , Judea Pearl , Bayes Theorem , Bayesian Inference , Bayes Nets , Deep Learning Neural Net , Dow Jones Index , Bayesian Nets , Bayes Net , Machine Intelligence And Ai , Analytics Planning And Data Analysis , தாமஸ் வளைகுடாக்கள் , ஜெனிபர் லாரன்ஸ் , இயந்திரம் கற்றல் , ஜூடியா முத்து ,