Results 41 to 50 of about 131,054 (221)

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

Universal efficiency at optimal work with Bayesian statistics

, 2010
If the work per cycle of a quantum heat engine is averaged over an appropriate prior distribution for an external parameter $a$, the work becomes optimal at Curzon-Ahlborn efficiency.
E. T. Jaynes, H. Jeffreys, H. Jeffreys, H. S. Leff, J. Gemmer, Ramandeep S. Johal   +5 more
core   +1 more source

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., Gamst, A. C., Kleijn, B. J. K., Ritov, Y.   +3 more
core   +3 more sources

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

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, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 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., Crooks G. E., Crooks G. E., Dorosz S., Gallavotti G., Gallavotti G., Gaspard P., Harris R. J., Hatano T., Jarzynski C., Jarzynski C., Komatsu T. S., Komatsu T. S., Komatsu T. S., Kurchan J., Lebowitz J. L., Maes C., Maragakis P., Nishimori H., Oono Y., Seifert U., Seifert U.   +21 more
core   +1 more source

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
core   +2 more sources

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
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

Network meta-analysis: application and practice using R software [PDF]

Epidemiology and Health, 2019
The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software.
Sung Ryul Shim, Seong-Jang Kim, Jonghoo Lee, Gerta Rücker   +3 more
doaj   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source