This paper reviews and contrasts the basic elements of statistical decision theory [1–4] and statistical learning theory [5–7]. It is not intended to be a comprehensive treatment of either subject, but rather just enough to draw comparisons between the two. Throughout this paper, let X denote the input to a decision-making process and Y denote the correct response or output (e.g., the value of ...

The proof of origin logs is becoming increasingly important. In the context Industry 4.0 and to combat illegal logging there an increasing motivation track each individual log. This work presents a two-stage convolutional neural network (CNN) based approach for wood log tracing on digital end images. First, cross section segmented from background by applying CNN-based segmentation method using ...

The James-Stein estimator is an of the multivariate normal mean and dominates maximum likelihood (MLE) under squared error loss. original work inspired great interest in developing shrinkage estimators for a variety problems. Nonetheless, research on estimation manifold-valued data scarce. In this paper, we propose parameters Log-Normal distribution defined manifold $N \times N$ symmetric posit...

We consider the problem of the optimal loss functions for predicted clickthrough rates in auctions for online advertising. While standard loss functions such as mean squared error or the log likelihood loss function severely penalize large mispredictions while imposing little penalty on smaller mistakes, we nd that a loss function re ecting the true underlying economic loss resulting from mispr...

Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly with the number of output classes, which can restrict the size of problems that we are able to tackle with current hardware. Second, it remains unclear how close it matches the task loss such as the top-k error rate or ...

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...

The problem of estimating the parameter θ, when it is restricted to an interval of the form [ ,1] m , in a class of discrete distributions, including Binomial ( , ), k θ Negative Binomial ( , ), r θ discrete Weibull ( ) θ and etc., is considered. We give necessary and sufficient conditions for which the Bayes estimator of , θ with respect to a two points boundary supported prior is minimax unde...

background and objectives: hbv dna monitoring is important in management of chronic viral hepatitis b infection. hbv dna measurements are carried out over period of months to years. so the analytical system must be stable and reproducible. the aim of this study was to determine the performance characteristics and to plan a statistical quality control system of a laboratory-developed real-time q...