This paper examines strategies for simulating exactly from large Gaussian linear models conditional on some Gaussian observations. Local computation strategies based on the conditional independence structure of the model are developed in order to reduced costs associated with storage and computation. Application of these algorithms to simulation from nested hierarchical linear models is conside...

We develop a penalized kernel smoothing method for the problem of selecting nonzero elements of the conditional precision matrix, known as conditional covariance selection. This problem has a key role in many modern applications such as finance and computational biology. However, it has not been properly addressed. Our estimator is derived under minimal assumptions on the underlying probability...

It is shown how to exactly simulate many-body interactions and multiqubit gates by coupling finite dimensional systems, e.g., qubits with a continuous variable. Cyclic evolution in the phase space of such a variable gives rise to a geometric phase, depending on a product of commuting operators. The latter allows one to simulate many-body Hamiltonians and nonlinear Hamiltonians, and to implement...

Detailed spatial models generated by conditional simulation provide a powerful tool for case-specific optimization of sampling designs. The entire process of sampling, estimation, and decision can be simulated on such a model by a Monte-Carlo approach. Optimization can be based on economic functions or on decision quality constraints rather than simple minimization of estimation variance. Effic...

The problem of estimating the location of facies boundaries is difficult in history matching, partly because of the geological complexity. The truncated plurigaussian method for modeling geologic facies is useful in this aspect not only for the wide variety of textures and shapes that can be generated, but also because of the internal consistency of the stochastic model. This method has not bee...

[1] This paper addresses subgrid variability, an issue that naturally arises in multiresolution numerical air quality models. Unlike previous approaches, which fit a parametric distribution over a spatial block and perform the fit from block to block independently over space and time, our approach to dealing with the subgrid variability is to describe the space-time conditional distribution of ...

In this paper we derive stochastic representations for the finite dimensional distributions of a multidimensional diffusion on a fixed time interval, conditioned on the terminal state. The conditioning can be with respect to a fixed point or more generally with respect to some subset. The representations rely on a reverse process connected with the given (forward) diffusion as introduced in Mil...

The Rényi entropy is a generalization of Shannon entropy to a one-parameter family of entropies. Tsallis entropy too is a generalization of Shannon entropy. The measure for Tsallis entropy is non-logarithmic. After the introduction of Shannon entropy , the conditional Shannon entropy was derived and its properties became known. Also, for Tsallis entropy, the conditional entropy was introduced a...

Consider a multivariate Gaussian random vector which can be partitioned into observed and unobserved components.We review a technique proposed almost twenty years ago in the astrophysics literature to sample from the posterior Gaussian distribution of the unobserved components given the observed components [6]. This technique can be computationally cheaper than the standard approach which requi...