This paper presents a servo strategy of Generalized Minimum Variance Control (GMVC) with a two degree of freedom. If GMVC fasts the transient response by tuning the control signal weight of the cost function, a large overshoot remains. This paper introduces the two degree of freedom GMVC. The two degree of freedom function curbs the overshoot, while the satisfactory transient characteristic hol...

This paper is an extension of EbayesThresh (the empirical bayesian thresholding method) under assumption of data with homogeneous standard deviation noises described in Johnstone and Silverman (2004)[1]. We ease this restriction, allowing heterogeneous standard deviation of noises, and provide details in estimating the true effects with posterior estimator, e.g. posterior mean or posterior medi...

For this problem, however, the usual approach would be completely inadequate since approximating θ to any reasonable degree of accuracy would require n to be inordinately large. For example, on average we would have to set n ≈ 2.7014× 10 in order to obtain just one non-zero value of I. Clearly this is impractical and a much smaller value of n would have to be used. Using a much smaller value of...

In reinforcement learning (RL), stochastic environments can make learning a policy difficult due to high degrees of variance. As such, variance reduction methods have been investigated in other works, such as advantage estimation and controlvariates estimation. Here, we propose to learn a separate reward estimator to train the value function, to help reduce variance caused by a noisy reward sig...

Evolutionary ecologists commonly use reaction norms, which show the range of phenotypes produced by a set of genotypes exposed to different environments, to quantify the degree of phenotypic variance and the magnitude of plasticity of morphometric and life-history traits. Significant differences among the values of the slopes of the reaction norms are interpreted as significant differences in p...