Results 41 to 50 of about 527,420 (298)

Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility [PDF]

, 2011
This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions ...
Jim E. Griffin, Griffin, Jim E., Delatola, Eleni-Ioanna, Eleni-Ioanna Delatola   +3 more
core   +1 more source

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

MXene‐Based Room‐Temperature NO2 Gas Sensors: A Meta‐Analysis

Advanced Science, EarlyView.
This study presents the first comprehensive meta‐analysis of MXene‐based NO2 sensors, decoding 32 study characteristics across 61 peer‐reviewed studies. By isolating materials chemistry as the primary performance driver over device‐level parameters, the authors establish a methodological blueprint and a predictive structure–function map to accelerate ...
Alexander Khort, Jerome Geyer‐Klingeberg, Andreas Rathgeber, Suelen Barg   +3 more
wiley   +1 more source

Bayesian and Nonparametric Bayesian Optimal Designs With the Length of Interconnected Intervals for Unit Exponential Regression Model

Journal of Probability and Statistics
Nonlinear regression models are widely utilized in many scientific disciplines. Precisely estimating an optimal nonlinear regression model is critical, especially when considering potential biases may arise in Bayesian optimal design.
Soleiman Khazaei, Anita Abdollahi Nanvapisheh, Habib Jafari   +2 more
doaj   +1 more source

Bayesian Non-Parametric Mixtures of GARCH(1,1) Models

Journal of Probability and Statistics, 2012
Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes ...
John W. Lau, Ed Cripps
doaj   +1 more source

Bayesian optimization for estimating the maximum tolerated dose in Phase I clinical trials

Contemporary Clinical Trials Communications, 2021
We introduce a Bayesian optimization method for estimating the maximum tolerated dose in this article. A number of parametric model-based methods have been proposed to estimate the maximum tolerated dose; however, parametric model-based methods need an ...
Ami Takahashi, Taiji Suzuki
doaj   +1 more source

Omnidirectional Interaction Networks Modulating Phenotypic Plasticity in the Gut Microbiota Towards Ulcerative Colitis

Advanced Science, EarlyView.
This study employs a spouse‐pair cohort design and an idopNetwork model that integrates evolutionary game theory, predator‐prey dynamics, and developmental modularity theory to reconstruct maximally informative microbial interaction networks. This approach reveals how interspecies interactions drive the phenotypic plasticity of the gut microbiota in ...
Huizhen Li, Jiayi Li, Ang Dong, Xiaoyan Guo, Qiuyue Tu, Zelin Feng, Haojia Lin, Yanru Zhang, Yuxuan Hao, Huiying Gong, Yihan Meng, Yu Wang, Shing‐Tung Yau, Xiaocang Cao, Rongling Wu, Xinjuan Liu   +15 more
wiley   +1 more source

A New Single Image Super-Resolution Method Based on the Infinite Mixture Model

IEEE Access, 2017
As a powerful nonparametric Bayesian model, the infinite mixture model has been successfully used in machine learning and computer vision. The success of the infinite mixture model owes to the capability clustering and density estimation.
Peitao Cheng, Yuanying Qiu, Xiumei Wang, Ke Zhao   +3 more
doaj   +1 more source

An AI‐Driven Framework for Directed Self‐Assembly Toward Design‐Technology Co‐Optimization

Advanced Science, EarlyView.
An artificial intelligence (AI)‐driven closed‐loop framework integrates experiment, three‐dimensional physics‐based simulation, and template design for directed self‐assembly (DSA). Guided by experimental data, AI‐assisted calibration accelerates model optimization, supports morphology prediction, and identifies template windows.
Caiwei Shang, Yanting Xu, Yue Zhang, Yan Zhang, Jianfeng Li, Ming Li, Longlin Lei, Wenda Bao, Weixuan Zeng, Shisheng Xiong   +9 more
wiley   +1 more source

Bayesian Varying-coefficient Models using Adaptive Regression Splines [PDF]

, 2000
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regression analysis. We present a fully Bayesian B-spline basis function approach with adaptive knot selection.
Ludwig Fahrmeir, Biller, Clemens, Clemens Biller, Fahrmeir, Ludwig   +3 more
core   +1 more source