Results 91 to 100 of about 627,252 (279)

Subjective confidence reflects representation of Bayesian probability in cortex. [PDF]

Nat Hum Behav, 2022
Geurts LS, Cooke JRH, van Bergen RS, Jehee JFM.   +3 more
europepmc   +1 more source

Machine‐Learning Microfluidic Minute‐Scale Microorganism Metrics Monitoring(M6)

Advanced Science, EarlyView.
ABSTRACT On‐site monitoring of microorganisms remains challenging because of low concentrations, strong background interference, and dynamic aerosol diffusion, particularly for aerosol‐transmitted pathogens. Here, we report a rapid detection platform that integrates a Puri‐focusing microfluidic chip, electrochemical impedance spectroscopy (EIS), and ...
Ning Yang, Jiahao Ding, Si Chen, Lijie Yan, Shichao Ding, Lavonda Li, Junyi Sun, Haodong Liu, Tongge Li, Ning Liu, Mingji Wei, Xiaoyong Zhu, Xiaobo Zou, Shouqi Yuan, Xingcai Zhang   +14 more
wiley   +1 more source

Toward Bayesian Classifiers with Accurate Probabilities [PDF]

, 2002
In most data mining applications, accurate ranking and probability estimation are essential. However, many traditional classifiers aim at a high classification accuracy (or low error rate) only, even though they also produce probability estimates. Does high predictive accuracy imply a better ranking and probability estimation?
Charles X. Ling, Huajie Zhang
openaire   +1 more source

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. [PDF]

Sci Rep, 2021
Kothari R, Jones V, Mena D, Bermúdez Reyes V, Shon Y, Smith JP, Schmolze D, Cha PD, Lai L, Fong Y, Storrie-Lombardi MC.   +10 more
europepmc   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Modifying Bayesian Networks by Probability Constraints

CoRR, 2005
Proceedings of the 21st Conference on Uncertainty in Artificial ...
Yun Peng 0001, Zhongli Ding
openaire   +3 more sources

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Comparison Between Bayesian and Frequentist Tail Probability Estimates

, 2019
In this paper, we investigate the reasons that the Bayesian estimator of the tail probability is always higher than the frequentist estimator. Sufficient conditions for this phenomenon are established both by using Jensen's Inequality and by looking at ...
González, Bárbara, Pericchi, Luis Raúl, Shen, Nan   +2 more
core  

BAYESIAN UPDATING OF ATOMIC PROBABILITIES

Economics Letters
The standard conditional probability formula is supposed to reflect the correct updating of probability assignments when new information is incorporated (Bayesian updating). We consider a context whith no preferences on outcomes. Starting from an atomic probability measure and assuming a “minimum requirement” relational assumption or other stronger ...
openaire   +1 more source

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network. [PDF]

Magn Reson Med, 2021
Koopman T, Martens R, Gurney-Champion OJ, Yaqub M, Lavini C, de Graaf P, Castelijns J, Boellaard R, Marcus JT.   +8 more
europepmc   +1 more source