In this paper we propose some moment matching pricing methods for European-style discrete arithmetic Asian basket options in a Black & Scholes framework. We generalize the approach of [5] and of [8] in several ways. We create a framework that allows for a whole class of conditioning random variables which are normally distributed. We moment match not only with a lognormal random variable but al...

Substructure coupling methods, such as the component mode synthesis (CMS) method, have been studied in structural dynamics analysis since 1960s. The modes of subsystems associated with the lowest frequencies are typically retained in these methods. In this paper, we present a coupling-matrix based mode selection scheme for the CMS method, referred to as the CMS method. This new scheme is derive...

Hidden Markov Models (HMMs) provide a simple framework to model the generative process of some types of sequential of data. We consider the challenging problem of estimating the parameters of an HMM incrementally by doing a constant amount of computation per observation. We propose an online moment matching algorithm for Bayesian learning of transition parameters of an HMM. While moment matchin...

Abstract—A mixed method for model order reduction is presented in this paper. The denominator polynomial is derived by matching both Markov parameters and time moments, whereas numerator polynomial derivation and error minimization is done using Genetic Algorithm. The efficiency of the proposed method can be investigated in terms of closeness of the response of reduced order model with respect ...

This paper presents sampling-based speech parameter generation using moment-matching networks for Deep Neural Network (DNN)-based speech synthesis. Although people never produce exactly the same speech even if we try to express the same linguistic and para-linguistic information, typical statistical speech synthesis produces completely the same speech, i.e., there is no inter-utterance variatio...

Generative moment matching network (GMMN), which is based on the maximum mean discrepancy (MMD) measure, is a generative model for unsupervised learning, where the mini-batch stochastic gradient descent is applied for the update of parameters. In this work, instead of obtaining a mini-batch randomly, each mini-batch in the iterations is selected in a submodular way such that the most informativ...

In this paper we present an analytical model for microand pico-cell wireless networks for any arbitrary topology in a high mobility environment. We introduce an approximation technique which uses a single cell decomposition analysis which incorporates moment matching of hand-off processes into the cell. The approximation technique is novel in that it can provide close approximations for non-Poi...

The aim of this paper is to present two moment matching procedures for basketoptions pricing and to test its distributional approximations via distances on the space of probability densities, the Kullback-Leibler information (KLI) and the Hellinger distance (HD). We are interested in measuring the KLI and the HD between the real simulated basket terminal distribution and the distributions used ...

Two approaches to moment matching based model reduction of aperiodically sampled data systems are given. The term “aperiodic sampling” is used in the paper to indicate that the time between two consecutive sampling instants can take its value from a pre-specified finite set of allowed sampling intervals. Such systems can be represented by discrete-time linear switched (LS) state space (SS) mode...