Covariance and stochastic mechanics

Identifiability of Dynamic Stochastic General Equilibrium Models with Covariance Restrictions

This article is concerned with identification problem of parameters of Dynamic Stochastic General Equilibrium Models with emphasis on structural constraints, so that the number of observable variables is equal to the number of exogenous variables. We derived a set of identifiability conditions and suggested a procedure for a thorough analysis of identification at each point in the parameters sp...

Covariance and Fisher information in quantum mechanics

Variance and Fisher information are ingredients of the Cramér-Rao inequality. We regard Fisher information as a Riemannian metric on a quantum statistical manifold and choose monotonicity under coarse graining as the fundamental property of variance and Fisher information. In this approach we show that there is a kind of dual one-to-one correspondence between the candidates of the two concepts....

Stochastic Covariance Models∗

A new class of stochastic covariance models based on Wishart distribution is proposed. Three categories of dynamic correlation models are introduced depending on how the timevarying covariance matrix is formulated and whether or not it is a latent variable. A stochastic covariance filter is also developed for filtering and predicting covariances. Extensions of the basic models enable the study ...

Stochastic Covariance Compression

Covariance matrices are an effective way to capture global spread across local interest points in images. Often, these image descriptors are more compact, robust and informative than, for example, bags of visual words. However, they are symmetric and positive definite (SPD) and therefore live on a non-Euclidean Riemannian manifold, which gives rise to non-Euclidean metrics. These are slow to co...

Stochastic Processes and Quantum Mechanics