Category: Nevin Manimala

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Nevin Manimala Statistics

statistics; +31 new citations

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These pubmed results were generated on 2020/06/15

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Nevin Manimala Statistics

What do ‘Bohemian Rhapsody,’ ‘Macbeth,’ and a list of Facebook friends all have in common?

A new study shows how vastly complex communication networks can efficiently convey large amounts of information to the human brain. Researcher found that works of literature, musical pieces, and social networks have a similar underlying structure that allows them to share information rapidly and effectively.
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Nevin Manimala Statistics

AI sentencing tools need to be closely scrutinized

Judges should closely vet the AI tools they use to help them predict whether a defendant is likely to re offend, urges a new study.
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Nevin Manimala Statistics

Limits on evolution revealed by statistical physics

What is and is not possible for natural evolution may be explained using models and calculations from theoretical physics, say researchers. To explain this the limits of evolution, researchers simplified the natural world to fit idealized physics models and searched for any mathematical structure within biological complexity.
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Nevin Manimala Statistics

Solution to century-old math problem could predict transmission of infectious diseases

An academic has achieved a milestone in statistical/mathematical physics by solving a 100-year-old physics problem — the discrete diffusion equation in finite space.
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Nevin Manimala Statistics

New model predicts the peaks of the COVID-19 pandemic

Researchers describe a single function that accurately describes all existing available data on active COVID-19 cases and deaths — and predicts forthcoming peaks.
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Nevin Manimala Statistics

High variability is result of complex data workflows

A new study offers new evidence that the complexity of contemporary analytical methods in science contributes to the variability of research outcomes.
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Nevin Manimala Statistics

New study estimates the odds of life and intelligence emerging beyond our planet

Despite knowing when life first appeared on Earth, scientists still do not understand how life occurred, which has important implications for the likelihood of finding life elsewhere in the universe. A new paper shows how an analysis using a statistical technique called Bayesian inference could shed light on how complex extraterrestrial life might evolve in alien worlds.
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Nevin Manimala Statistics

Identifying light sources using artificial intelligence

Identifying sources of light plays an important role in the development of many photonic technologies, such as lidar, remote sensing, and microscopy. Traditionally, identifying light sources as diverse as sunlight, laser radiation, or molecule fluorescence has required millions of measurements, particularly in low-light environments, which limits the realistic implementation of quantum photonic technologies. Researchers demonstrated a smart quantum technology that enables a dramatic reduction in the number of measurements required to identify light sources.
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Nevin Manimala Statistics

Bayesian group selection in logistic regression with application to MRI data analysis.

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Bayesian group selection in logistic regression with application to MRI data analysis.

Biometrics. 2020 May 04;:

Authors: Lee K, Cao X

Abstract
We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only a small portion of groups are significant, thus consistent group selection is of significant importance. While consistent frequentist group selection methods have been proposed, theoretical properties of Bayesian group selection methods for logistic regression models have not been investigated yet. In this paper, we consider a hierarchical group spike and slab prior for logistic regression models in high-dimensional settings. Under mild conditions, we establish strong group selection consistency of the induced posterior, which is the first theoretical result in the Bayesian literature. Through simulation studies, we demonstrate that the proposed method outperforms existing state-of-the-art methods in various settings. We further apply our method to an MRI data set for predicting Parkinson’s disease and show its benefits over other contenders. This article is protected by copyright. All rights reserved.

PMID: 32365231 [PubMed – as supplied by publisher]