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It has recently been shown that Bayesian integration of probability distributions represented in neuronal population codes such as the one used in our model may be achieved by simple summation of population activities [5].
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The latter operation is performed in the block "Integration of probabilities".
The proposed classification scheme uses the integration of probabilities by dividing a non-stationary signal of length N into M segments of length L. As a result, a sequence of M quasi stationary signals is obtained.
A one-dimensional integral approach (ODIA) consisting of sampling, evaluation of statistical moments for multivariable functions, probability density function fitting, and simple integration of failure probability was developed through system integration.
The failure probability is then analyzed using the convoluted integration of the probability density distribution functions governing the two random variables.
The approximation of assuming a "first order" limit state function is assessed by comparison with direct integration of the probability distribution function in the failure region.
However, the equations of the M-step (update of the parameters) need the integration of the probability values as listed in the Equations 10-13.
The exact expressions are presented in two different ways, Laplace transform and by direct integration of joint probability density function of eigenvalues coming from the quotient ensemble.
The premium rate also can be determined through integration of the probability distribution simulated with yield records (Ozaki et al. 2008).
The integration of Weibull probability density function is known as cumulative distribution function and can be expressed as: Fleft( v right) = mathop int nolimits_{0}^{v} fleft( v right) {text{d}}v = 1 - e^{{ - left( {frac{v}{c}} right)^{k} }} (2).
Here we obtained results by integration of the probability in time for one period and divide by the period : <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0017888.e032.PNG" class= inline-graphic"/>.
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