scholarly journals INFLUENCE OF STATISTICAL FLUCTUATIONS ON K/π RATIOS IN RELATIVISTIC HEAVY ION COLLISIONS

2001 ◽  
Vol 16 (07) ◽  
pp. 1227-1235 ◽  
Author(s):  
C. B. YANG ◽  
X. CAI
Keyword(s):  
Negative Binomial ◽  
Heavy Ion ◽  
Relativistic Heavy Ion Collisions ◽  
Relative Variance ◽  
Ion Collisions ◽  
Statistical Fluctuations ◽  
Binomial Distributions ◽  
The Mean ◽  
Multiplicity Distributions ◽  
Statistical Background

The influence of pure statistical fluctuations on K/π ratio is investigated in an event-by-event way. Poisson and the modified negative binomial distributions are used as the multiplicity distributions since they both have statistical background. It is shown that the distributions of the ratio in these cases are Gaussian, and the mean and relative variance are given analytically.

scholarly journals Statistical background subtraction using spatial cues

2007 ◽  
Vol 17 (12) ◽  
pp. 1758-1763 ◽  
Author(s):  
Pierre-Marc Jodoin ◽  
Max Mignotte ◽  
Janusz Konrad
Keyword(s):  
Background Subtraction ◽  
Spatial Cues ◽  
Statistical Background

scholarly journals Visualizing hematopoiesis as a stochastic process

2018 ◽  
Vol 2 (20) ◽  
pp. 2637-2645
Author(s):  
Jason Xu ◽  
Yiwen Wang ◽  
Peter Guttorp ◽  
Janis L. Abkowitz
Keyword(s):  
Stochastic Process ◽  
Cell Expansion ◽  
Visualization Tool ◽  
Graphical Interface ◽  
Human Cord Blood ◽  
Virtual Experiments ◽  
Clonal Hematopoiesis ◽  
Cord Blood Cell ◽  
Statistical Background ◽  
User Friendly

Abstract Stochastic simulation has played an important role in understanding hematopoiesis, but implementing and interpreting mathematical models requires a strong statistical background, often preventing their use by many clinical and translational researchers. Here, we introduce a user-friendly graphical interface with capabilities for visualizing hematopoiesis as a stochastic process, applicable to a variety of mammal systems and experimental designs. We describe the visualization tool and underlying mathematical model, and then use this to simulate serial transplantations in mice, human cord blood cell expansion, and clonal hematopoiesis of indeterminate potential. The outcomes of these virtual experiments challenge previous assumptions and provide examples of the flexible range of hypotheses easily testable via the visualization tool.

scholarly journals Fundamentals of Clinical Prediction Modeling for the Neurosurgeon

Neurosurgery ◽  
2019 ◽  
Vol 85 (3) ◽  
pp. 302-311 ◽  
Author(s):  
Hendrik-Jan Mijderwijk ◽  
Ewout W Steyerberg ◽  
Hans-Jakob Steiger ◽  
Igor Fischer ◽  
Marcel A Kamp
Keyword(s):  
Prediction Models ◽  
Model Development ◽  
Scientific Work ◽  
Evidence Based ◽  
Clinical Prediction ◽  
Prediction Modeling ◽  
Clinical Prediction Models ◽  
Prognostic Variables ◽  
Basic Understanding ◽  
Statistical Background

AbstractClinical prediction models in neurosurgery are increasingly reported. These models aim to provide an evidence-based approach to the estimation of the probability of a neurosurgical outcome by combining 2 or more prognostic variables. Model development and model reporting are often suboptimal. A basic understanding of the methodology of clinical prediction modeling is needed when interpreting these models. We address basic statistical background, 7 modeling steps, and requirements of these models such that they may fulfill their potential for major impact for our daily clinical practice and for future scientific work.

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