Results 61 to 70 of about 190,737 (267)

Clinical, histological, and serological predictors of renal function loss in lupus nephritis.

Arthritis Care &Research, Accepted Article.
Objective Kidney survival is the ultimate goal in lupus nephritis (LN) management, but long‐term predictors remain inadequately studied, requiring long‐term follow‐up. This study aimed to identify baseline and early longitudinal predictors of kidney survival in the Accelerating Medicines Partnership LN longitudinal cohort.
Shangzhu Zhang, Laurence Magder, Daniel Goldman, Judith A. James, Joel M Guthridge, Carla Guthridge, Peter Izmirly, Jill P Buyon, H.Michael Belmont, Richard A. Furie, Noa Schwartz, Chaim Putterman, Jennifer L. Barnas, Jennifer H. Anolik, Sarah French, Maria Dall'era, Avi Z. Rosenberg, Jeffrey Hodgin, Dawit S. Demeke, the Accelerating Medicines Partnership RA/SLE Network, Michelle Petri, Andrea Fava   +21 more
wiley   +1 more source

Task-evoked pupillary responses track precision-weighted prediction errors and learning rate during interceptive visuomotor actions

Scientific Reports, 2022
In this study, we examined the relationship between physiological encoding of surprise and the learning of anticipatory eye movements. Active inference portrays perception and action as interconnected inference processes, driven by the imperative to ...
D. J. Harris, T. Arthur, S. J. Vine, J. Liu, H. R. Abd Rahman, F. Han, M. R. Wilson   +6 more
doaj   +1 more source

Bayesian Active Learning for Classification and Preference Learning

, 2011
Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute.
Houlsby, Neil, Huszár, Ferenc, Ghahramani, Zoubin, Lengyel, Máté   +3 more
openaire   +2 more sources

Bayesian approaches to associative learning: From passive to active learning [PDF]

Learning & Behavior, 2008
Traditional associationist models represent an organism's knowledge state by a single strength of association on each associative link. Bayesian models instead represent knowledge by a distribution of graded degrees of belief over a range of candidate hypotheses.
openaire   +2 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

A Network Intrusion Detection Method Incorporating Bayesian Attack Graph and Incremental Learning Part

Future Internet, 2023
For the current stage of complex and changing network environments and correlated and synchronized vulnerability attacks, this study first fuses attack graph technology and Bayesian networks and constructs Bayesian attack graphs toportray the correlation
Kongpei Wu, Huiqin Qu, Conggui Huang
doaj   +1 more source

Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection

, 2018
High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g.
Li, Longhai, Yao, Weixin
core   +2 more sources

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models

Frontiers in Psychology, 2022
Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information ...
Endris Assen Ebrahim, Endris Assen Ebrahim, Mehmet Ali Cengiz   +2 more
doaj   +1 more source

Efficient variational Bayesian model updating by Bayesian active learning

Mechanical Systems and Signal Processing
As a main task of inverse problem, model updating has received more and more attention in the area of inspection, sensing, and monitoring technologies during the recent decades, where the estimation of posterior probability density function (PDF) of unknown model parameters is still challenging for expensive-to-evaluate models of interest.
Fangqi Hong, Pengfei Wei, Sifeng Bi, Michael Beer   +3 more
openaire   +2 more sources