Results 101 to 110 of about 348,082 (329)
A Note of Caution on Maximizing Entropy
Entropy, 2014 The Principle of Maximum Entropy is often used to update probabilities due to evidence instead of performing Bayesian updating using Bayes’ Theorem, and its use often has efficacious results.
Richard E. Neapolitan, Xia Jiang
doaj +1 more source
Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. [PDF]
Sci Rep, 2021 Kothari R +10 more
europepmc +1 more source
A Bayesian Approach to Learning Bayesian Networks with Local Structure [PDF]
arXiv, 2013 Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has concentrated on using decision-tree representations for the CPDs.
arxiv
Product differentiation in the fruit industry: Lessons from trademarked apples
Agribusiness, EarlyView.Abstract We derive price premiums for patented or trademarked apple varieties, also known as “club apples,” compared to open‐variety apples. We use an expansive retail scanner dataset, along with unique data on apple taste characteristics, to estimate monthly club apple premiums for 2008–2018.
Modhurima Dey Amin +3 more
wiley +1 more source
On the relevance of prognostic information for clinical trials: A theoretical quantification
Biometrical Journal, Volume 65, Issue 1, January 2023., 2023 Abstract The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalization.
Sandra Siegfried +2 more
wiley +1 more source
Bayesian nets and medical diagnosis. A different way to learn conditional probabilities
Modelling in Science Education and Learning, 2019 Bayesian networks are a formal tool that allows to model processes characterized by uncertainty, which is typical of many real problems. A Bayesian network can establish a comprehensive model on a set of random variables and their relationships.
Vicente Domingo Estruch Fuster +3 more
doaj +1 more source
Agribusiness, EarlyView.Abstract Eco‐labels inform consumers about the sustainable attributes of a product, but consumer face challenges to differentiate and select for specific attributes. Certification programs are similarly challenged to incentivize adoption of sustainable practices in product supply chains when consumer ability to differentiate sustainable attributes is ...
Nicolas Gatti +5 more
wiley +1 more source
Bayesian time‐varying autoregressive models of COVID‐19 epidemics
Biometrical Journal, Volume 65, Issue 1, January 2023., 2023 Abstract The COVID‐19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time‐dependent Poisson autoregressive models that include time‐varying coefficients to estimate the effect of policy ...
Paolo Giudici +2 more
wiley +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 +8 more
europepmc +1 more source
Quantum Mechanics as an Exotic Probability Theory [PDF]
arXiv, 1995 Recent results suggest that quantum mechanical phenomena may be interpreted as a failure of standard probability theory and may be described by a Bayesian complex probability theory.
arxiv