marginal distribution sentence in Hindi
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- The conditional distribution of the joint concomitants can be derived from the above result by comparing the formula in marginal distribution and hence
- Joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables.
- By construction, the marginal distribution over \ tau is a gamma distribution, and the conditional distribution over x given \ tau is a Gaussian distribution.
- We assume that the statistics of the process are known completely, that is, the marginal distribution of the process seen at each time instant is known.
- If X | \ mu has an NEF-QVF distribution and & mu; has a conjugate prior distribution then the marginal distributions are well-known distributions.
- Likewise, the estimated joint marginal distribution of the set of variables belonging to one factor is proportional to the product of the factor and the messages from the variables:
- The normal-inverse Gaussian distribution can also be seen as the marginal distribution of the normal-inverse Gaussian process which provides an alternative way of explicitly constructing it.
- Upon convergence ( if convergence happened ), the estimated marginal distribution of each node is proportional to the product of all messages from adjoining factors ( missing the normalization constant ):
- Then the choice of the marginal distribution p _ X ( x ) completely determines the joint distribution p _ { X, Y } ( x, y ) due to the identity
- George Seber points out that the Wishart distribution is not called the multivariate chi-squared distribution because the marginal distribution of the off-diagonal elements is not chi-squared.
- Therefore no finite value can be selected with more than a 50 % chance of being above " N " ( the marginal distribution of " N " ).
- The term "'nuisance variable "'is sometimes also used in more general contexts, simply to designate those variables that are marginalised over when finding a marginal distribution.
- The package allows the updating of a " N "-dimensional array with respect to given target marginal distributions ( which, in turn can be multi-dimensional ).
- Some correlation statistics, such as the rank correlation coefficient, are also invariant to monotone transformations of the marginal distributions of " X " and / or " Y ".
- The marginal distribution can be transformed into the density matrix and / or the Wigner function give information about the quantum state of the photon, we have reconstructed the quantum state of the photon.
- For multimodal marginal distributions ( a beam profile with multiple peaks ), the 1 / e 2 width usually does not yield a meaningful value and can grossly underestimate the inherent width of the beam.
- The prediction is not just an estimate for that point, but also has uncertainty information-- it is a one-dimensional Gaussian distribution ( which is the marginal distribution at that point ).
- The marginal distribution of each of the X _ i variables is negative binomial, as the X _ i count ( considered as success ) is measured against all the other outcomes ( failure ).
- In probability theory and its applications, factor graphs are used to represent factorization of a probability distribution function, enabling efficient computations, such as the computation of marginal distributions through the sum-product algorithm.
- In the Bayesian derivation of the marginal distribution of an unknown normal mean \ mu above, \ sigma as used here corresponds to the quantity \ scriptstyle { s / \ sqrt { n } }, where
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