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Examples ofmarginal distribution
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Figure 3 shows that themarginaldistributionof each of the posterior realizations of the residuals are fairly close to the standard normal distribution.
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The phenotypic variance (variance of the 2 2marginaldistributionof y) is s2=sa+exp(b+sa/2).
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The smoothing vector is not themarginaldistribution, as used in the traditional language model.
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Fig. 3 also shows amarginaldistributionof the ratio of responses to first- and second-order motion.
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Thus, a more detailed study would examine changes to the whole of themarginaldistribution.
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This example is from Wikipedia and may be reused under a CC BY-SA license.It calculates themarginaldistributionfor each unobserved node, conditional on any observed nodes.
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This example is from Wikipedia and may be reused under a CC BY-SA license.The measured curve is the integral of themarginaldistribution, and starts at the total beam power and decreases monotonically to zero power.
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This example is from Wikipedia and may be reused under a CC BY-SA license.The fidelity deals with themarginaldistributionof the random variables.
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This example is from Wikipedia and may be reused under a CC BY-SA license.Furthermore, themarginaldistributionof any subset of variables can be approximated by simply examining the samples for that subset of variables, ignoring the rest.
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This example is from Wikipedia and may be reused under a CC BY-SA license.The conditional distribution contrasts with themarginaldistributionof a random variable, which is its distribution without reference to the value of the other variable.
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This example is from Wikipedia and may be reused under a CC BY-SA license.The messages coming from both sides are required to calculate themarginaldistributionfor a node.
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This example is from Wikipedia and may be reused under a CC BY-SA license.The main idea in the proof is the continuity of the mutual information in the pairwisemarginaldistribution.
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This example is from Wikipedia and may be reused under a CC BY-SA license.That value is then normalized by the maximum possible level of agreement given the marginal distributions.
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We will demonstrate that we can extend the marginal distributions of 0 to a measure with the desired properties.
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For the stillbirths the two marginal distributions were more similar.
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Figure 9 shows that the marginal distributions of income in the prepolicy state and the postpolicy state are essentially identical.
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The biggest difference across the four samples occurs in the marginal distributions for discussion frequency.
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The current procedure only exploits information in the marginal distributions of pricing errors.
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