Results 61 to 70 of about 425,037 (266)

Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution

Discrete Dynamics in Nature and Society, 2021
In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed.
Kang Li, Xian-ming Shi, Juan Li, Mei Zhao, Chunhua Zeng   +4 more
doaj   +1 more source

Sparse Stochastic Inference for Latent Dirichlet allocation

, 2012
We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference.
Blei, David, Hoffman, Matt, Mimno, David
core   +2 more sources

Single‐Cell Metabolic Imaging and Digital Scoring of Fat Tissue Remodeling by Label‐Free Metabolic Microscopy

Advanced Science, EarlyView.
Mid‐infrared optoacoustic microscopy (MiROM) acquires lipid‐ and protein‐ associated vibrational contrast in intact fat tissue without dyes, preserving native tissue architecture. Through lateral and axial segmentation, MiROM tracks intrinsic intracellular changes during postnatal remodeling. A quantitative spatial analysis tool (Q‐SAT) maps white‐ and
Myeongseop Kim, Constantin Berger, Alexander Wolf, Alina Peteranderl, Martin Klingenspor, Vasilis Ntziachristos, Yongguo Li, Miguel A. Pleitez   +7 more
wiley   +1 more source

Computational statistics using the Bayesian Inference Engine

, 2012
This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise
Babu, Berntsen, Feroz, Gelman, Gelman, Gelman, Geyer, Giakoumatos, Green, Gregory, Grubbs, Hastings, Hobson, Jeffreys, Kass, Kirkpatrick, Lewis, Lindley, Liu, Lu, Lu, Martin D. Weinberg, Metropolis, Neal, Neal, Newton, Pearson, Press, Price, Raftery, Robert, Skilling, Storn, Storn, Sérsic, Ter Braak, Verdinelli, Wall, Weinberg, Weinberg, Yoon   +40 more
core   +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

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling [PDF]

, 2016
We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed ...
Bach, Francis, Dupuy, Christophe
core   +3 more sources

Bayesian Nonparametric Weighted Sampling Inference

, 2015
It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights.
Gelman, Andrew, Pillai, Natesh S., Si, Yajuan   +2 more
core   +1 more source

Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics

Advanced Science, EarlyView.
Weak nonlinearities critically shape the long term behavior of oscillatory systems but are difficult to identify from data. A data‐driven framework is introduced to infer governing equations of weakly nonlinear oscillators from sparse and noisy observations.
Teng Ma, Ting‐Ting Gao, Wei Cui, Attilio Frangi, Gang Yan, Lin Zhao   +5 more
wiley   +1 more source

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

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics

Advanced Science, EarlyView.
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang, Bingqian Dai, Shijie Xu, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Xixiang Zhang, Kang L. Wang   +11 more
wiley   +1 more source