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The theory of the velocity of the transmission of the pulse through the circulation dates back to 1808 with the work of Thomas Young. [9] The relationship between pulse wave velocity (PWV) and arterial wall stiffness can be derived from Newton's second law of motion (=) applied to a small fluid element, where the force on the element equals the product of density (the mass per unit volume ...
The Moens–Korteweg equation states that PWV is proportional to the square root of the incremental elastic modulus, (E inc), of the vessel wall given constant ratio of wall thickness, h, to vessel radius, r, and blood density, ρ, assuming that the artery wall is isotropic and experiences isovolumetric change with pulse pressure. [5]
In statistics and in particular statistical theory, unbiased estimation of a standard deviation is the calculation from a statistical sample of an estimated value of the standard deviation (a measure of statistical dispersion) of a population of values, in such a way that the expected value of the calculation equals the true value.
For normally distributed random variables inverse-variance weighted averages can also be derived as the maximum likelihood estimate for the true value. Furthermore, from a Bayesian perspective the posterior distribution for the true value given normally distributed observations and a flat prior is a normal distribution with the inverse-variance weighted average as a mean and variance ().
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
In probability and statistics, a partition of the sampling space into disjoint events is described by the probabilities assigned to such events. The vector of D probabilities can be considered as a composition of D parts. As they add to one, one probability can be suppressed and the composition is completely determined.
The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.
The notation AR(p) refers to the autoregressive model of order p.The AR(p) model is written as = = + where , …, are parameters and the random variable is white noise, usually independent and identically distributed (i.i.d.) normal random variables.