Results 71 to 80 of about 43,983 (273)

Patterns of Scalable Bayesian Inference

, 2016
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge.
Adams, Ryan P., Angelino, Elaine, Johnson, Matthew James   +2 more
core   +1 more source

Variational Inference for Large Bayesian Vector Autoregressions

Journal of Business & Economic Statistics
We propose a novel variational Bayes approach to estimate high-dimensional Vector Autoregressive (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural representation of the parameter space for posterior inference.
Bernardi, Mauro, Bianchi, Daniele, Bianco, Nicolas   +2 more
openaire   +5 more sources

Dissecting the Ecological Structure of Health and Disease in the Global Gut Microbiome

Advanced Science, EarlyView.
We introduce Wiredancer, a framework that identifies three continuous ecological factors of the gut microbiota. These factors exhibit distinct patterns across health and disease, jointly capturing disrupted ecological stability and offering a new perspective for precision diagnostics and therapeutic strategies.
Baoyuan Zhu, Shuhao Chen, Yunheng Diao, Wei Wang, Yuanyuan Huang, Liqin Liang, Xiaodan Lu, Rui Han, Minxin Guo, Zhaobo Li, Shuhong Wang, Hehua Li, Chenyu Liu, Jing Zhou, Dongsheng Xiong, Xiaobo Li, Yuping Ning, Xuetao Shi, Fengchun Wu, Kai Wu   +19 more
wiley   +1 more source

SKOOTS: Skeleton‐Oriented Object Segmentation for Mitochondria in High‐Resolution Cochlear EM Datasets

Advanced Science, EarlyView.
Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka, Richard T. Osgood, Hidetomi Nitta, Artur A. Indzhykulian   +3 more
wiley   +1 more source

Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks

Nature Communications, 2023
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment.
Djohan Bonnet, Tifenn Hirtzlin, Atreya Majumdar, Thomas Dalgaty, Eduardo Esmanhotto, Valentina Meli, Niccolo Castellani, Simon Martin, Jean-François Nodin, Guillaume Bourgeois, Jean-Michel Portal, Damien Querlioz, Elisa Vianello   +12 more
doaj   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

Natural Variation of NAR5 Determines Nitrogenase Activity and the Yield in Soybean

Advanced Science, EarlyView.
This study identified NAR5, a gene encoding a subtilisin‐like protease, that regulates nitrogenase activity in soybean nodules. Overexpressing NAR5 delayed nodule senescence, enhancing nitrogenase activity, yield, and low‐nitrogen tolerance. The elite haplotype NAR5HapI‐1 linked to superior nitrogenase activity and greater seed weight has been ...
Chao Ma, Hanyu Zhao, Haojie Feng, Xulun Dong, Lin Chen, Mingliang Yang, Runyi Chen, Chengjun Lei, Chunyan Liu, Qingshan Chen, Dawei Xin, Jinhui Wang   +11 more
wiley   +1 more source

Variational Bayesian Quantile Regression with Non-Ignorable Missing Response Data

Axioms
For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant ...
Juanjuan Zhang, Weixian Wang, Maozai Tian   +2 more
doaj   +1 more source

Variational prior replacement in Bayesian inference and inversion

Geophysical Journal International
SUMMARYMany scientific investigations require that the values of a set of model parameters are estimated using recorded data. In Bayesian inference, information from both observed data and prior knowledge is combined to update model parameters probabilistically by calculating the posterior probability distribution function.
Xuebin Zhao, Andrew Curtis
openaire   +3 more sources

Comparative Oligo‐FISH Mapping Illuminates Chromosomal Evolution Among Rutaceae Species Diverged Over 50 Million Years

Advanced Science, EarlyView.
Oligonucleotide‐based fluorescence in situ hybridization probes were developed in the model citrus species Citrus maxima. These probes were applied to comparative karyotyping across 14 species in the Rutaceae family. This analysis revealed chromosomal evolution in lineages that diverged from Citrus nearly 52 million years ago.
Li He, Hainan Zhao, Xiaoxue Zeng, Zujun Yang, Wenting Li, Guoyan Zhang, Guangrong Li, Jian He, Bin Guan, Jiming Jiang   +9 more
wiley   +1 more source