Results 41 to 50 of about 131,402 (244)
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.
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Advanced Energy Materials, EarlyView.In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu +6 more
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Bayesian Posteriors Without Bayes' Theorem [PDF]
, 2012 The classical Bayesian posterior arises naturally as the unique solution of several different optimization problems, without the necessity of interpreting data as conditional probabilities and then using Bayes' Theorem.
Dall'Aglio, Marco, Hill, Theodore P.
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The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry
Agribusiness, EarlyView.ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
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Sharpening the Second Law of Thermodynamics with the Quantum Bayes' Theorem [PDF]
, 2014 We prove a generalization of the classic Groenewold-Lindblad entropy inequality, combining decoherence and the quantum Bayes theorem into a simple unified picture where decoherence increases entropy while observation decreases it.
Gharibyan, Hrant, Tegmark, Max
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Advanced Intelligent Systems, EarlyView.This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
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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
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The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA? [PDF]
, 2014 We consider the Bayesian analysis of a few complex, high-dimensional models and show that intuitive priors, which are not tailored to the fine details of the model and the estimated parameters, produce estimators which perform poorly in situations in ...
Bickel, P. J. +3 more
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Optimal model‐based design of experiments for parameter precision: Supercritical extraction case
The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study investigates the process of chamomile oil extraction from flowers. A parameter‐distributed model consisting of a set of partial differential equations is used to describe the governing mass transfer phenomena in a cylindrical packed bed with solid chamomile particles under supercritical conditions using carbon dioxide as a solvent ...
Oliwer Sliczniuk, Pekka Oinas
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Predicting cervical cancer DNA methylation from genetic data using multivariate CMMP
Canadian Journal of Statistics, EarlyView.Abstract Epigenetic modifications link the environment to gene expression and play a crucial role in tumour development. DNA methylation, in particular, is gaining attention in cancer research, including cervical cancer, the focus of this study.
Hang Zhang +5 more
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