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• Probability density function
  ...unction <math> F_\phi(\Phi) </math> of a scalar <math> \phi </math> is the probability The probability of finding <math> \phi </math> in a range <math> \Phi_1,\Phi_2 </math>
  3 KB (564 words) - 16:03, 20 May 2011
• Introduction to turbulence/Statistical analysis/Probability
  == The histogram and probability density function == ...m will become less erratic and will be more representative of the actual ''probability'' of occurrence of the amplitudes of the signal itself, as long as the wind
  10 KB (1,686 words) - 16:30, 31 August 2007

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• Introduction to turbulence
  * [[Introduction to turbulence/Statistical analysis/Probability|Probability]] ...lysis/Probability#Histogram and probability density function|Histogram and probability density function]]
  8 KB (948 words) - 12:34, 15 March 2012
• Introduction to turbulence/Statistical analysis
  *[[Introduction to turbulence/Statistical analysis/Probability|Probability]] ...lysis/Probability#Histogram and probability density function|Histogram and probability density function]]
  4 KB (544 words) - 18:06, 25 June 2007
• Introduction to turbulence/Statistical analysis/Multivariate random variables
  ...ensity function from its measurable counterpart, the histogram, a '''joint probability density function''' (or '''jpdf'''),<math>B_{uv}</math> , can be built-up f As with the single-variable pdf, there are certain conditions the joint probability density function must satisfy. If <math>B_{uv}\left( c_{1}c_{2} \right)</ma
  10 KB (1,494 words) - 12:41, 21 June 2007
• Fluent FAQ
  | PDF || Probability Density Function
  39 KB (6,386 words) - 16:48, 26 April 2013
• Combustion
  where <math> P(Z,\chi) </math> is the joint [[Probability density function | Probability Density Function]] (PDF) of the mixture fraction the [[probability density function]] is replaced by a [[subgrid PDF]] <math> \widetilde{P}</m
  156 KB (25,897 words) - 22:14, 17 March 2011
• Probability density function
  ...unction <math> F_\phi(\Phi) </math> of a scalar <math> \phi </math> is the probability The probability of finding <math> \phi </math> in a range <math> \Phi_1,\Phi_2 </math>
  3 KB (564 words) - 16:03, 20 May 2011
• Conditional filtering
  <math> \psi_\eta </math> is a fine-grained [[probability density function]], The probability density function
  1 KB (202 words) - 12:38, 8 May 2006
• Subgrid PDF
  A subgrid [[probability density function]] <math> \bar{P}(\eta) </math> , The probability of observing values between <math> \eta < Z < \eta + d\eta </math>
  1 KB (261 words) - 07:39, 12 April 2007
• Beta PDF
  A <math> \beta </math> [[probability density function]] depends on
  835 B (129 words) - 10:05, 17 December 2008
• Introduction to turbulence/Statistical analysis/Probability
  == The histogram and probability density function == ...m will become less erratic and will be more representative of the actual ''probability'' of occurrence of the amplitudes of the signal itself, as long as the wind
  10 KB (1,686 words) - 16:30, 31 August 2007
• Introduction to turbulence/Statistical analysis/Ensemble average
  ...ime. The key is that they must all be ''independent events'' - meaning the probability of achieving a head or tail in a given flip must be completely independent {{Chapter navigation||Probability}}
  8 KB (1,391 words) - 12:29, 2 July 2011
• Introduction to turbulence/Statistical analysis/Estimation from a finite number of realizations
  ...never an infinite number of realizations from which ensemble averages (and probability densities) can be computed, it is essential to ask: ''How many realizations ...ds, does the estimator ''converge'' in a statistical sense (or converge in probability). <font color="orange">Figure 2.9</font> illustrates the problems which can
  6 KB (935 words) - 16:42, 31 August 2007
• Introduction to turbulence/Statistical analysis/Generalization to the estimator of any quantity
  ...ussian distributed random variable, we know from the previous section on [[Probability in turbulence#Skewness and kurtosis|skewness and kurtosis]] that the kirtos
  3 KB (526 words) - 17:30, 25 June 2007
• PHOENICS
  ...n PHOENICS is it's Multi-Fluid Model (MFM) which can be used to ''derive'' Probability Density Functions for turbulent flow problems (although only fairly basic v
  11 KB (1,641 words) - 11:18, 5 July 2013
• Shape Design Optimization
  ...d global properties and are able to find the global optimum with very high probability. IOSO Technology was designed to solve very complex optimization tasks.
  3 KB (398 words) - 14:55, 7 January 2009
• Introduction to turbulence/Study questions
  ... to zero and as the number of realizations becomes infinite, show that the probability average defined by <math>\left\langle x^{n} \right\rangle = \int^{\infty}_
  5 KB (752 words) - 12:38, 21 June 2007
• Introduction to turbulence/Stationarity and homogeneity
  ...e \tilde{ u \left( t \right)} \right\rangle = U </math>, etc. In fact, the probability density itself is time-independent, as should be obvious from the fact that
  41 KB (6,536 words) - 09:19, 25 February 2008