portfolio theory assumes that investors accept risk. this means thatin the equal rate of return on the two assets, the assets were chosenthat have a lower risk level. modern portfolio theory is accepted byinvestors who believe that they are not cope with the market. sothey keep many different types of securities in order to access theoptimum efficiency rate that is close to the rate of return o...

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...

Vapnik described the “three main learning problems” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functions used to evaluate performance (0-1 loss, squared loss and log loss respectively). But there are many other loss functions one could use. In this chapter I will summarise some recent work by myself and colleagues studying the th...

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...

We propose a quartic function to represent a family of continuous quality loss functions. Depending on the choice of its parameters the shape of this function within the specification limits can be either symmetric or asymmetric, and it can be either similar to the ubiquitous quadratic loss function or somewhat closer to the conventional step function. We examine this family of loss functions i...

There are several reasons to evaluate a multi-class classifier on other measures than just error rate. Perhaps most importantly, there can be uncertainty about the exact context of classifier deployment, requiring the classifier to perform well with respect to a variety of contexts. This is commonly achieved by creating a scoring classifier which outputs posterior class probability estimates. P...