Results 51 to 60 of about 1,294,144 (288)

Expert System for Diagnosis Chicken Disease using Bayes Theorem

Journal of Physics: Conference Series, 2019
Chicken disease is a condition in which the body organs of chickens cannot function normally. Diagnosing a disease requires symptoms that appear on the chicken’s body. To conduct a disease diagnosis, further examination is needed by specialists (experts).
Hengki Tamando Sihotang
semanticscholar   +1 more source

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics

Advanced Science, EarlyView.
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang, Bingqian Dai, Shijie Xu, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Xixiang Zhang, Kang L. Wang   +11 more
wiley   +1 more source

Penalaran Berbasis Aturan Untuk Diagnosa Awal Penyakit Anjing Menggunakan Teorema Bayes

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2018
Many pets can be played with, socialize and even live together with humans. Numbers of animal clinics have increased to provide care for pets. This study focuses on Dog as pet. Desease and improper treatment of dog will adversely affect the Dog.
Sella Marselena, Ause Labellapansa, Abdul Syukur   +2 more
doaj   +1 more source

Mass‐Produced and High‐Performance Nanowell Biosensor Fabricated via Semiconductor Manufacturing for Rapid and Accurate COVID‐19 Diagnosis in the Clinical Field

Advanced Science, EarlyView.
A semiconductor‐fabricated nanowell biosensor enables rapid, scalable, and highly reproducible detection of SARS‐CoV‐2 antigens from nasal swabs within ∼10 minutes. Clinical validation in 249 retrospective and 243 prospective patient samples demonstrates high sensitivity and specificity, minimal cross‐reactivity, and robust batch‐to‐batch ...
Yoo Min Park, Zahra Rezaei, Nam Ho Bae, Donggee Rho, Da‐Seul Kim, EunYoung Go, YoungTae Seo, Seok Jae Lee, Won Chan Seo, Luke P. Lee, HeaYeon Lee, Su Ryon Shin   +11 more
wiley   +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

Enhancing Bayesian Approaches in the Cognitive and Neural Sciences via Complex Dynamical Systems Theory

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

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