Results 201 to 210 of about 17,065,182 (226)

Identification of acute kidney injury after non-cardiac surgery by machine learning leveraging a large electronic health record database. [PDF]

Sci Rep
Leins J, Meyer H, Engel L, Hinze C, Gillmann HJ, Stüber T, Zacharias HU.   +6 more
europepmc   +1 more source

Do pastoral and agro-pastoral perceptions align with observed climate extremes? Evidence from the Koh-e-Suleiman Range, Pakistan. [PDF]

Sci Rep
Tareen WUH, Schlecht E.
europepmc   +1 more source

An automated framework to classify skin lesions using Multi-Head Self Attention Layer-based Vision Transformers. [PDF]

Front Artif Intell
Faizal S, Rajput CA, Prusty MR.
europepmc   +1 more source

Linking visceral fat accumulation to gut microbiota: key bacterial taxa and their roles in the glycogen synthesis pathway. [PDF]

Exp Mol Med
Kim NE, Cho B, Kim Y, Kim G, Yie GE, Kim H, Park J, Kim H, Jeong C, Lee M, Joo Y, Lim S, Park S, Hong Y, Min K, Cho B, Yun JM, Jung ES, Ji Y, Cho S, Seong JK, Park H, Park JH, Won S.   +23 more
europepmc   +1 more source

Supervised learning for predicting unknown modifying variables in pliable lasso. [PDF]

Sci Rep
Hawrami ZSM, Cengiz MA, Dünder E.
europepmc   +1 more source

Multinomial Processing Tree Models for Discrete Choice

Zeitschrift Fur Psychologie / Journal of Psychology, 2009
This paper shows how to develop new multinomial processing tree (MPT) models for discrete choice, and in particular binary choice. First it reviews the history of discrete choice with special attention to Duncan Luce\u27s book Individual Choice Behavior.
Xiangen Hu, William H Batchelder
exaly   +2 more sources

Multinomial processing tree models: An implementation

Behavior Research Methods, 1999
Multinomial processing tree (MPT) models have been widely used by researchers in cognitive psychology. This paper introduces MBT.EXE, a computer program that makes MPT easy to use for researchers MBT.EXE implements the statistical theory developed by Hu ...
Xiangen Hu
exaly   +2 more sources

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Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach

Psychometrika, 2023
Steffen Nestler, Edgar Erdfelder
exaly  

Tree Inference: Response Time and Other Measures in a Binary Multinomial Processing Tree, Representation and Uniqueness of Parameters

Mathematics, 2022
Xiaofang Zheng, Richard Schweickert
exaly