Results 41 to 50 of about 43,983 (273)
Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring
Due to the impact of air quality on health, the use of low-cost gas sensor systems in air quality monitoring has increased. The deficiencies of low-cost gas sensors such as cross-sensitivities, interferences with environmental factors, and unit-to-unit ...
Georgi Tancev, Federico Grasso Toro
doaj +1 more source
BayesPy: Variational Bayesian Inference in Python
Submitted to Journal of Machine Learning Research - Machine Learning Open Source ...
openaire +7 more sources
Automatic Variational Inference in Stan [PDF]
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate.
Blei, David M. +3 more
core
Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales ...
Deutsch, Matthaeus +3 more
core +1 more source
Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
wiley +1 more source
Toward Variational Structural Learning of Bayesian Networks
This study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables.
Andres R. Masegosa, Manuel Gomez-Olmedo
doaj +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Variational Bayesian Variable Selection for High-Dimensional Hidden Markov Models
The Hidden Markov Model (HMM) is a crucial probabilistic modeling technique for sequence data processing and statistical learning that has been extensively utilized in various engineering applications.
Yao Zhai +3 more
doaj +1 more source
Platform motion estimation in multi-band synthetic aperture sonar with coupled variational autoencoders [PDF]
Coherent processing in synthetic aperture sonar (SAS) requires platform motion estimation and compensation with sub-wavelength accuracy for high-resolution imaging.
Angeliki Xenaki +2 more
doaj +1 more source
Variational Bayesian Inference Time Delay Estimation for Passive Sonars
In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal ...
Feilong Ding +3 more
doaj +1 more source

