Results 101 to 110 of about 2,103,105 (303)

Objective Bayes estimation and hypothesis testing : the reference-intrinsic approach [PDF]

, 2005
Conventional frequentist solutions to point estimation and hypothesis testing typically need ad hoc modifications when dealing with non-regular models, and may prove to be misleading.
Juárez, Miguel A.
core  

Locally Adaptive Function Estimation for Binary Regression Models [PDF]

, 2003
In this paper we present a nonparametric Bayesian approach for fitting unsmooth or highly oscillating functions in regression models with binary responses. The approach extends previous work by Lang et al. (2002) for Gaussian responses.
Jerak, A., Jerak, Alexander, Lang, S., Lang, Stefan   +3 more
core   +1 more source

Bayesian ad non-Bayesian estimation for parameters of WPM distribution:C.

, 2023
Bayesian ad non-Bayesian estimation for parameters of WPM distribution:C.
Salemah A. Almutlak (14322088), Eslam Hussam (14026349), Ramy Aldallal (14026346), Muqrin A. Almuqrin (14322085), Kadir Karakaya (14322091), Ahmed M. Gemeay (11185379), Nicholas Makumi (14322094), Ehab M. Almetwally (13993998)   +7 more
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Bayesian ad Non-Bayesian estimation for parameters of WPM distribution:D.

, 2023
Bayesian ad Non-Bayesian estimation for parameters of WPM distribution:D.
Salemah A. Almutlak (14322088), Eslam Hussam (14026349), Ramy Aldallal (14026346), Muqrin A. Almuqrin (14322085), Kadir Karakaya (14322091), Ahmed M. Gemeay (11185379), Nicholas Makumi (14322094), Ehab M. Almetwally (13993998)   +7 more
core   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Bayesian econometrics:conjugate analysis and rejection sampling using mathematica [PDF]

, 1992
Mathematica is a powerful "system for doing mathematics by computer" which runs on personal computers (Macs and MS-DOS machines), workstations and mainframes. Here we show how Bayesian methods can be implemented in Mathematica.
Ley, Eduardo, Steel, Mark F.J.
core   +1 more source

Comparison of some Bayesian estimation methods for type-I generalized extreme value distribution with simulation

Alexandria Engineering Journal
The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play
Sundos Bader Habeeb, Fatima K. Abdullah, Rehab Noori Shalan, Amal S. Hassan, Ehab M. Almetwally, Fatimah M. Alghamdi, Sara Mohamed Ahmed Alsheikh, Md Moyazzem Hossain   +7 more
doaj   +1 more source

BAYESIAN CURVE ESTIMATION BY MODEL AVERAGING [PDF]

A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the ...
M. Dolores Redondas, Daniel Peña
core  

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source