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In general, the evaluation of complicated stochastic systems is difficult only from the viewpoint of conventional structural approach based on their physical internal mechanisms. In this case, we adopt very often the regression analysis model between input and output signals from the viewpoint of functional approach. In the previous paper, an extended regression analysis method was proposed by considering...
Parameter estimation plays an important role in systems biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more intense as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. A novel stochastic parameter estimation method has been developed where the aim is to obtain the optimal parameters corresponding...
Estimation of the differential entropy from observations of a random variable is of great importance for a wide range of signal processing applications such as source coding, pattern recognition, hypothesis testing, and blind source separation. In this paper, we present a method for estimation of the Shannon differential entropy that accounts for embedded manifolds. The method is based on high-rate...
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