Results 81 to 90 of about 2,020,572 (302)

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Using stacking to average bayesian predictive distributions (with discussion) [PDF]

, 2018
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of ...
Grunwald, Peter, Castro-Camilo, Daniela, Li, Meng, Shin, Minsuk, Grushka-Cockayne, Yael, Gelman, Andrew, Lichtendahl, Kenneth C., Iacopini, Matteo, Dawid, A. Philip, van Dijk, Herman K., Lichtendahl, Kenneth C, Yao, Yuling, Zhou, Tianjian, Winkler, Robert L, Aastveit, Knut Are, Dawid, A Philip, Yoo, William Weimin, Pericchi, Luis, Clarke, Bertrand, Bakka, Haakon C, Freni-Sterrantino, Anna, Franck, Christopher T., Hoogerheide, Lennart, Tonellato, Stefano, Winkler, Robert L., Simpson, Daniel, Ferreira, Marco A. R., Franco-Villoria, Maria, Belitser, Eduard, Bakka, Haakon C., van Dijk, Herman K, Ferreira, Marco AR, de Heide, Rianne, Nurushev, Nurzhan, Jose, Victor Richmond R., Vehtari, Aki, Clyde, Merlise, McAlinn, Kenichiro, West, Mike, Rue, Havard, Huser, Raphael, Opitz, Thomas, Jose, Victor Richmond R, Franck, Christopher T   +43 more
core   +1 more source

Bayesian Sperm Competition Estimates

Genetics, 2003
Abstract We introduce a Bayesian method for estimating parameters for a model of multiple mating and sperm displacement from genotype counts of brood-structured data. The model is initially targeted for Drosophila melanogaster, but is easily adapted to other organisms. The method is appropriate for use with field studies where the number
Beatrix, Jones, Andrew G, Clark
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

Bayesian Learning of Markov Network Structure [PDF]

, 2006
We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina, Aleks Jakulin, Jakulin, Aleks, Irina Rish   +3 more
core   +2 more sources

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Bayesian Estimation of Unknown Heteroscedastic Variances [PDF]

We propose a Bayesian procedure to estimate possibly heteroscedastic variances of the regression error term, without assuming any structure on them. What we propose in this paper, may be construed as a Conditional Bayesian procedure that is conditioned ...
Tsunemasa Shiba, Hiroaki Chigira
core  

Statistical inference for nonignorable missing-data problems: a selective review

Statistical Theory and Related Fields, 2018
Nonignorable missing data are frequently encountered in various settings, such as economics, sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection ...
Niansheng Tang, Yuanyuan Ju
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Selection effect of learning rate parameter on estimators of k exponential populations under the joint hybrid censoring

Heliyon
A Bayesian method based on the learning rate parameter η is called a generalized Bayesian method. In this study, joint hybrid censored type I and type II samples from k exponential populations were examined to determine the influence of the parameter η ...
Yahia Abdel-Aty, Mohamed Kayid, Ghadah Alomani   +2 more
doaj   +1 more source