Results 1 to 10 of about 548,736 (273)

Weyl Prior and Bayesian Statistics [PDF]

Entropy, 2020
When using Bayesian inference, one needs to choose a prior distribution for parameters. The well-known Jeffreys prior is based on the Riemann metric tensor on a statistical manifold.
Ruichao Jiang, Javad Tavakoli, Yiqiang Zhao   +2 more
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Global Geometry of Bayesian Statistics [PDF]

Entropy, 2020
In the previous work of the author, a non-trivial symmetry of the relative entropy in the information geometry of normal distributions was discovered. The same symmetry also appears in the symplectic/contact geometry of Hilbert modular cusps. Further, it
Atsuhide Mori
doaj   +2 more sources

Bibliometric Analysis of Surgical Articles Using Bayesian Statistics [PDF]

Annals of Surgery Open
Objectives:. The study aims to investigate the landscape and trends in the use of Bayesian statistics in surgical papers published in high-impact journals over the past 2 decades, determine the characteristics of these papers, and assess the quality of ...
Zhenyu Li, MSc, Aliya Izumi, HBSc, Dominique Vervoort, MD, MPH, CPH, MBA, Kuan Liu, PhD, Stephen E. Fremes, MD, MSc   +4 more
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Cosmological Parameter Inference with Bayesian Statistics

Universe, 2021
Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found their place in the field of Cosmology. They have become important mathematical and numerical tools, especially in parameter estimation and model comparison.
Luis E. Padilla, Luis O. Tellez, Luis A. Escamilla, Jose Alberto Vazquez   +3 more
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Frequentist against Bayesian statistics: tug of war! [PDF]

Acute and Critical Care, 2022
Abhijit Nair
doaj   +2 more sources

Bayesian statistics for clinical research [PDF]

Lancet, The
Goligher EC, Heath A, Harhay MO.
exaly   +2 more sources

Bayesian Statistics [PDF]

Journal of Applied Statistics, 2013
Classical statistics involves ways to test hypotheses and estimate confidence intervals. Bayesian statistics involves methods to calculate probabilities associated with your hypotheses. The result is a posterior distribution that combines information from your data with prior beliefs.
James B. Elsner, Thomas H. Jagger
  +6 more sources

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Entropy, 2022
Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem ...
Zahra Amini Farsani, Volker J. Schmid
doaj   +1 more source

Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients

Critical Care Explorations, 2021
OBJECTIVES:. To evaluate factors predictive of clinical progression among coronavirus disease 2019 patients following admission, and whether continuous, automated assessments of patient status may contribute to optimal monitoring and management. DESIGN:.
Suchi Saria, PhD, Peter Schulam, PhD, Brian J. Yeh, PhD, Daniel Burke, MD, MBA, Sean D. Mooney, PhD, Christine T. Fong, MS, Jacob E. Sunshine, MD, Dustin R. Long, MD, Vikas N. O’Reilly-Shah, MD, PhD   +8 more
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Bayesian Uncertainty Quantification for Channelized Reservoirs via Reduced Dimensional Parameterization

Mathematics, 2021
In this article, we study uncertainty quantification for flows in heterogeneous porous media. We use a Bayesian approach where the solution to the inverse problem is given by the posterior distribution of the permeability field given the flow and ...
Anirban Mondal, Jia Wei
doaj   +1 more source