We consider density-dependent Markov chains converging, as the scale parameter K>0 goes to infinity, solution of an ODE admitting exponentially stable equilibrium point. provide a new strong approximation density by Gaussian process, based on construction Kurtz using Komlós–Major–Tusnády theorem. show that given any threshold ?(K)?1 greater than multiple log(K)/K, time error needs reach ?(K) is...

Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

Mendel (1866) suggested that if many heritable "factors" contribute to a trait, near-continuous variation could result. Fisher (1918) clarified the connection between Mendelian inheritance and continuous trait variation by assuming many loci, each with small effect, and by informally invoking the central limit theorem. Barton et al. (2017) rigorously analyze the approach to a multivariate Gauss...

We consider multi-dimensional Gaussian processes and give a new condition on the covariance, simple and sharp, for the existence of Lévy area(s). Gaussian rough paths are constructed with a variety of weak and strong approximation results. Together with a new RKHS embedding, we obtain a powerful yet conceptually simple framework in which to analysize differential equations driven by Gaussian si...

Non-Gaussian/non-linear data assimilation is becoming an increasingly important area of research in the Geosciences as the resolution and non-linearity of models are increased and more and more non-linear observation operators are being used. In this study, we look at the effect of relaxing the assumption of a Gaussian prior on the impact of observations within the data assimilation system. Thr...

‎in this paper, we formulate the fourth order sturm-liouville problem (fslp) as a lie group matrix differential equation. by solving this ma- trix differential equation by lie group magnus expansion, we compute the eigenvalues of the fslp. the magnus expansion is an infinite series of multiple integrals of lie brackets. the approximation is, in fact, the truncation of magnus expansion and a gauss...

The value algorithms of classical function approximation theory have a common drawback: the compute-intensive, poor adaptability, high model and data demanding and the limitation in practical applications. Neural network can calculate the complex relationship between input and output, therefore, neural network has a strong function approximation capability. This paper describes the application ...