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Shuffle of min’s random variable approximations of bivariate copulas’realization
Copula Shuffle of Min approximation Narrow bounds of copula
2016/1/26
The comonotonicity and countermonotonicity provide intuitive upper and lower depen-dence relationship between random variables. This paper constructs the shuffle of min’s ran-domvariableapproximations...
Shuffle of min’s random variable approximations of bivariate copulas’realization
random variable approximations bivariate copulas realization
2016/1/20
The comonotonicity and countermonotonicity provide intuitive upper and lower depen-dence relationship between random variables. This paper constructs the shuffle of min’s ran-domvariableapproximations...
This paper discusses some of the merits of the strong approximation ideas in developing diffusion approximations for queueing systems. Letting ρ be the utilization of the server, it is well known that...
Diffusion Approximations for the Maximum of a Perturbed Random Walk
Perturbed random walk diffusion approximation light-tailed distributi
2015/7/6
Considera random walk S=(Sn:n≥O) that is "perturbed" by a stationary sequence (ξn:n≥O) to produce the process S=(Sn+ξn:n≥O). In this paper, we are concerned with developing limit theorems and approxim...
Complete Corrected Diffusion Approximations for the Maximum of a Random Walk
Corrected diffusion approximations random walks ladder heights single-server queue
2015/7/6
Consider a random walk (Sn: n ≥ 0) with drift −μ and S0= 0. Assuming that the increments have exponential moments, negative mean, and are strongly nonlattice, we provide a complete asymptotic ex...
Uniform Approximations for the M/G/1 Queue with Subexponential Processing Times
Uniform approximations M/G/1 queue subexponential distributions heavy traffi c heavy tails Cram′er series
2015/7/6
This paper studies the asymptotic behavior of the steady-state waiting time, W∞, of the M/G/1 queue with subexponenential processing times for different combinations of traffic intensities and overflo...
On the Convergence of Finite Order Approximations of Stationary Time Series
Wide sense stationary time series autoregressive estimate moving average estimate
2015/7/6
The approximation of a stationary time-series by finite order autoregressive (AR) and moving averages (MA) is a problem that occurs in many applications. In this paper we study asymptotic behavior of ...
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Sparse approximations in spatio-temporal point-process models
latent Gaussian models linear dynamical systems log Gaussian Cox process approximate inference expectation propagation sparse inference
2013/6/14
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Understanding Operational Risk Capital Approximations: First and Second Orders
Basel II/III Capital Approximation Loss Distributional Approach Capital Approximation Value-at-Risk Expected Shortfall Spectral Risk Measure Subexponential Regularly Varying
2013/5/2
We set the context for capital approximation within the framework of the Basel II / III regulatory capital accords. This is particularly topical as the Basel III accord is shortly due to take effect. ...
We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of specia...
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Convergence asymptotic normality variational Bayesian approximations exponential family models missing values
2012/9/19
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
Sharp analysis of low-rank kernel matrix approximations
Sharp analysis low-rank kernel matrix approximations
2012/9/18
We consider supervised learning problems within the positive-definite kernel framework,such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading t...