Results 141 to 150 of about 15,712,923 (297)

Default Bayesian model determination methods for generalised linear mixed models

, 2010
In this paper, we consider a default strategy for fully Bayesian model determination for GLMMs. We address the two key issues of default prior specification and computation.
Overstall, Anthony M., Overstall, Antony, Forster, Jonathan, Overstall, Antony M., Forster, Jonathan J.   +4 more
core   +1 more source

Enhancing Amphibious Flipper‐Based Locomotion Through Terrain‐Dependent Bayesian Optimization of Central Pattern Generator Control

Advanced Robotics Research, EarlyView.
A sea turtle‐inspired amphibious robot uses simulation‐guided optimization to adapt its flipper coordination to different environments. Sensitivity analysis and multiobjective Bayesian optimization identify terrain‐dependent controllers that are validated on hardware.
Nnamdi C. Chikere, Yasemin Ozkan‐Aydin
wiley   +1 more source

Consensus Formation and Change are Enhanced by Neutrality

Advanced Science, EarlyView.
Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag, Janina A. Hoffmann, Tim Rogers, Christian A. Yates   +3 more
wiley   +1 more source

Bayesian active learning with model selection for spectral experiments

Scientific Reports
Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments.
Tomohiro Nabika, Kenji Nagata, Masaichiro Mizumaki, Shun Katakami, Masato Okada   +4 more
doaj   +1 more source

Bayesian model selection for variable-coefficient partial differential equation discovery

Results in Engineering
Data-driven discovery of partial differential equations (PDEs) has emerged as a promising approach for identifying underlying physics when domain knowledge about observed data is limited. Despite recent progress, the identification of governing equations
Pongpisit Thanasutives, Yoshinobu Kawahara, Ken-ichi Fukui   +2 more
doaj   +1 more source

Bayesian Model Averaging in R [PDF]

Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited.
Christopher F. Parmeter, Shahram Amini
core  

Integrating Genetics With Epidemiological Measurements Identifies Burden QTLs of Inflammatory Bowel Disease across 20 Countries

Advanced Science, EarlyView.
The burden quantitative trait locus (bQTL) analysis integrates epidemiological data from GBD study with genotype of 2 621 978 SNPs from 2414 individuals in 1000 Genomes Project across 20 countries. rs7633471 shows the strongest effect that each increase in centi‐allele frequency of the allele C associated with 10.00 decrease of age‐standardized ...
Chen Sun, Siyu Wei, Junxian Tao, Haiyan Chen, Chi Yan, Jiacheng Wang, Jing Xu, Lian Duan, Yuanbo Zhan, Yuping Zou, Ruilin Li, Linna Yuan, Wei She, Songlin Lu, Ning Wang, Qinduo Ren, Yan Guo, Yuan Xu, Chang Wang, Hongsheng Tian, Chen Zhang, Yu Dong, Guoping Tang, Zhenwei Shang, Yongshuai Jiang, Wenhua Lv, Mingming Zhang, Hongchao Lyu   +27 more
wiley   +1 more source

Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection. [PDF]

Psychol Methods, 2020
Williams DR, Rast P, Pericchi LR, Mulder J.   +3 more
europepmc   +1 more source

Bayesian stochastic model specification search for seasonal and calendar effects [PDF]

We apply a recent methodology, Bayesian stochastic model specification search (SMSS), for the selection of the unobserved components (level, slope, seasonal cycles, trading days effects) that are stochastically evolving over time.
Tommaso, Proietti, Stefano, Grassi
core  

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source