Stimulus4ependent changes have been observed in the correlations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. A simple neural model can reproduce most of the characteristics of these experimental observations based on model neurons having leaky integration and fire-and-reset ...

Although market observations show that correlation between stocks, interest rates, e. g., is not deterministic, correlation is usually modelled as a fixed number. This article provides a new approach of modelling correlation as a stochastic process which has applications in many fields. We will show an example from financial markets where the stochasticity of correlation is a fundamental source...

We study canonical correlation analysis (CCA) as a stochastic optimization problem. We show that regularized CCA is efficiently PAC-learnable. We give stochastic approximation (SA) algorithms that are instances of stochastic mirror descent, which achieve -suboptimality in the population objective in time poly( 1 , 1 δ , d) with probability 1− δ, where d is the input dimensionality.

We consider two stochastic process methods for performing canonical correlation analysis (CCA). The first uses a Gaussian Process formulation of regression in which we use the current projection of one data set as the target for the other and then repeat in the opposite direction. The second uses a Dirichlet process of Gaussian models where the Gaussian models are determined by Probabilistic CC...

A new class of foreign equity option pricing model is suggested that not only allows for the volatility but also for the correlation coefficient to vary stochastically over time. A modified Jacobi process is proposed to evaluate risk premium of the stochastic correlation, and a partial differential equation to price the correlation risk for the foreign equity has been set up, whose solution has...

For the study of discharge activities of neurons, appropriate-ness and the basic role of certain non-stationary stochastic point-process models are appraised. Measures of association (cross-correlations) of spike trains for two or more neurons are also considered. Incorporating random intensity functions, some doubly stochastic Poisson process representations for the counting processes (and his...

The difficulties of simulating non-Gaussian stochastic processes to follow arbitrary product–moment covariance models and arbitrary non-Gaussian marginal distributions are well known. This paper proposes to circumvent these difficulties by prescribing a fractile correlation function, rather than the usual product–moment covariance function. This fractile correlation can be related to the produc...

In this article we discuss a method to complete the correlation matrix in a multi-dimensional stochastic volatility model. We concentrate on the construction of a positive definite correlation matrix. Furthermore we present a numerical integration scheme for this system of stochastic differential equations which improves the approximation quality of the standard Euler-Maruyama method with minim...

naturally occurred soil deposits inherit heterogeneity and anisotropy in their strength properties. the main purpose of this paper is to model the soil stratum with anisotropy consideration and spatially varying undrained shear strength by using random field theory coupled with finite difference numerical analysis to evaluate their effect on the bearing capacity of the shallow foundations. in t...

Default dependency structure is crucial in pricing multi-name credit derivatives as well as in credit risk management. In this paper, we extend the first passage model for one name with stochastic volatility (Fouque-Sircar-Sølna, Applied Mathematical Finance 2006) to the multi-name case. Correlation of defaults is generated by correlation between the Brownian motions driving the individual name...