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About 10,000 papers for “Probability density function”

FEV and FVC probability density function estimation.

Sara Zarei (652678), Ali Mirtar (652679), Forest Rohwer (7845) et al. · Figshare · 2015

<p>The x-axis shows the predicted FEV values and the y-axis is the predicted FVC values for a hypothetical the synthetic lung example described in the text.</p>

Cited by 0Open access

A device for probability density function measurements

P. Ottonello · Journal of Physics E Scientific Instruments · 1974

The working principle of an analogue instrument for probability density function measurements is presented together with the results obtained by a simple and inexpensive prototype.

Cited by 2

PROBABILITY DENSITY FUNCTION CONTROL OF QUANTUM SYSTEMS

Yifan Xing, Jun Wu · International Journal of Modern Physics B · 2011

This paper proposes a new method of controlling quantum systems via probability density function (PDF) control. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed for the general case and limited input energy, respectively. Unlike traditional quantum control methods, this method directly controls the probability distribution of the quantum state. It provides an alternative method for quantum control engineering.

Cited by 2

Probability density function of adjusted mutual information.

Oksana Semenova (5439332), Gordon Lightbody (5439341), John M. O’Toole (5439329) et al. · Figshare · 2018

<p>AMI for real data (MAP and sub-band energy) (green) and its permuted surrogates (black); toy data (artificially created correlated signals) (red) and its permuted surrogates (grey). Distributions of surrogates are clipped.</p>

Cited by 0Open access

Facts about the gaussian probability density function

Brian Aldershof, J. S. Marron, B.U. Park et al. · Applicable Analysis · 1995

The Gaussian probability density function plays a central role in probability, statistics and Fourier analysis. This paper presents formulas involving various combinations of moments, derivatives, integral, products and convolutions of this function. The results are useful in statistical curve estimation, but also have a beauty of their own.

Cited by 25

Quantifying consciousness through intrinsic probability density function

Norden E. Huang, Wenping Yuan, Albert C. Yang et al. · Biological Psychology · 2025

Consciousness remains a multifaceted phenomenon that is difficult to be measured by traditional quantification methods. Here we propose the intrinsic probability density function (iPDF) as a quantitative method to evaluate the dynamic inter-cortical interactions that underlie conscious states. First, the method utilizes empirical mode decomposition to derive intrinsic mode functions (IMFs) from EEG signals. Then, the method generates scale-dependent probability density functions for successive partial sums of IMFs that can capture subtle variations in neural modulation patterns. We tested the iPDF analysis across various consciousness states such as general anesthesia, distinct sleep stages (wakefulness, REM, and deep sleep), sensory conditions (eyes open versus eyes closed), and between dementia patients and healthy subjects. Our findings reveal that active neural interactions or modulations during wakefulness and REM sleep are characterized by super-Gaussian iPDF patterns. By contrast, the reduced interactions observed in anesthesia and deep sleep yield near-Gaussian iPDF profiles. We also present a classification model built on iPDF features that achieved an accuracy of approximately 87 % in distinguishing dementia patients from health controls, demonstrating the iPDF as a potential biomarker in clinical screening. This study supports the idea that consciousness emerges from complex, scale-dependent neural processes and presents a robust, quantitative framework that may enhance both our theoretical understanding and practical assessment of various states of consciousness.

Cited by 2Open access

Probability density function of turbulent velocity fluctuations

Hideaki Mouri, Masanori Takaoka, Akihiro Hori et al. · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2002

The probability density function (PDF) of velocity fluctuations is studied experimentally for grid turbulence in a systematical manner. At small distances from the grid, where the turbulence is still developing, the PDF is sub-Gaussian. At intermediate distances, where the turbulence is fully developed, the PDF is Gaussian. At large distances, where the turbulence has decayed, the PDF is hyper-Gaussian. The Fourier transforms of the velocity fluctuations always have Gaussian PDFs. At intermediate distances from the grid, the Fourier transforms are statistically independent of each other. This is the necessary and sufficient condition for Gaussianity of the velocity fluctuations. At small and large distances, the Fourier transforms are dependent.

Cited by 30Open access

Probability Density Function of Wind Power Variations

Weixing Lin, Jinyu Wen, Xiaomeng Ai et al. · 2012

Variations is an intrinsic property of wind power,but few common methods are reported that quantificationally define the variations.Based on the field measurement,it's found that t location-scale distribution is suitable to identify the probability distribution of wind power variations.Analysis shows that the minute-scale wind power is about 2% to 5% of the installed capacity.Summation of the outputs from different wind farms regional apart could effectively decrease the percentage of minute-scale wind power.The types of wind farms have a little influence on the percentage of minute-scale wind power,and the influence of the present total output is negligible.The t location-scale distribution is also capable to describe the probability distribution of variations of average wind power of adjacent time intervals.As the length of average time interval increases,the variations increase due to the decrease of the correlation between wind velocities.

Cited by 41

The Joint Scalar Probability Density Function Method

W.P. Jones · 2002

The purpose of this chapter is to review the joint probability density function, (PDF), evolution equation approach to calculating the properties of turbulent flames. In this approach the closure of the micromixing term represents a central difficulty and for this, various proposals including the linear mean square estimation closure, coalescence-dispersion models and Langevin, binomial, mapping and Euclidean minimum spanning tree closures are appraised. Results obtained with the PDF equation using a modified coalescence-dispersion model and a simple global reaction mechanism demonstrate that the method is capable of reproducing measured mixture fraction, fuel and CO2 profiles in jet diffusion flames and counterflow premixed and partially premixed turbulent flames. The levels of carbon monoxide are somewhat overpredicted in all the flames considered but it is argued that this is primarily a consequence of limitations in the simplified chemical reaction mechanism used. The need for improved models to represent micromixing is also suggested.

Cited by 8

Quantum Computation Based Probability Density Function Estimation

Ferenc Balázs, Sándor Imre · ArXiv.org · 2004

Signal processing techniques will lean on blind methods in the near future, where no redundant, resource allocating information will be transmitted through the channel. To achieve a proper decision, however, it is essential to know at least the probability density function (pdf), which to estimate is classically a time consumption and/or less accurate hard task, that may make decisions to fail. This paper describes the design of a quantum assisted pdf estimation method also by an example, which promises to achieve the exact pdf by proper setting of parameters in a very fast way.

Cited by 0Open access

Probability density function of the PIW distribution.

Sukanya Yodnual (21956982), Jularat Chumnaul (17537898) · Figshare · 2025

The figure shows the probability density function (PDF) of the PIW distribution for various combinations of the parameters and .

Cited by 0Open access