Results 51 to 60 of about 131,180 (321)
Advanced Energy and Sustainability Research, EarlyView.This study proposes a method to increase the value of solar power in balancing markets by managing prediction errors. The approach models prediction uncertainties and quantifies reserve requirements based on a probabilistic model. This enables the more reliable participation of photovoltaic plants in balancing markets across multiple sites, especially ...
Jindan Cui +3 more
wiley +1 more source
Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
Sensors, 2018 Optimizing an experimental design is a complex task when a model is required for indirect reconstruction of physical parameters from the sensor readings.
Guillermo Rus, Juan Melchor
doaj +1 more source
Dynamics, 2023 In the cognitive and neural sciences, Bayesianism refers to a collection of concepts and methods stemming from various implementations of Bayes’ theorem, which is a formal way to calculate the conditional probability of a hypothesis being true based on ...
Luis H. Favela, Mary Jean Amon
doaj +1 more source
Asymptotic Accuracy of Bayesian Estimation for a Single Latent Variable [PDF]
, 2015 In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent ...
Yamazaki, Keisuke
core +1 more source
From Droplet to Diagnosis: Spatio‐Temporal Pattern Recognition in Drying Biofluids
Advanced Intelligent Systems, EarlyView.This article integrates machine learning (ML) with the spatio‐temporal evolution of biofluid droplets to reveal how drying and self‐assembly encode distinctive compositional fingerprints. By leveraging textural features and interpretable ML, it achieves robust classification of blood abnormalities with over 95% accuracy.
Anusuya Pal +2 more
wiley +1 more source
Posterior probability and fluctuation theorem in stochastic processes
, 2009 A generalization of fluctuation theorems in stochastic processes is proposed. The new theorem is written in terms of posterior probabilities, which are introduced via the Bayes theorem.
Crooks G. E. +21 more
core +1 more source
Advanced Physics Research, EarlyView.Cd2SnO4 exhibits excellent thermoelectric properties with a high Seebeck coefficient, power factor, and figure of merit, surpassing Bi2Te3. It shows both positive and negative Seebeck coefficient values, making it suitable for diverse applications. Its high electrical conductivity and low thermal conductivity enhance efficiency, while its negative Hall
Adel Bandar Alruqi, Nicholas O. Ongwen
wiley +1 more source
BAT - The Bayesian Analysis Toolkit
, 2008 We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution.
Akaike +14 more
core +1 more source
Generative Deep Learning for Advanced Battery Materials
Batteries &Supercaps, EarlyView.This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal +3 more
wiley +1 more source
DIAGNOSING PESTS AND DISEASES ON PINEAPPLE USING THE BAYES THEOREM
Russian Journal of Agricultural and Socio-Economic Sciences, 2023 Pineapple plants grow in tropical climates and have long been cultivated. Pineapple plants can be harvested 18-24 months after planting. Pineapple contains vitamins A and C and calcium, phosphorus, magnesium, iron, sodium, potassium, dextrose, sucrose ...
Wahyuni M.S., Marbun B.P.T., Riansah W.
doaj +1 more source