CANONICAL REPRESENTATION OF STATIONARY QUANTUM GAUSSIAN PROCESSES

State Space representation of non-stationary Gaussian Processes

The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest. The main reason is that it allows to compute GPs based inferences in O(n), where n is the number of observations. This implementation makes GPs suitable for Big Data. For this reason, it is important to provide a SS representation of the most important kernels used in machine learning. The aim ...

Stationary Systems of Gaussian Processes

We describe all countable particle systems on R which have the following three properties: independence, Gaussianity, and stationarity. More precisely, we consider particles on the real line starting at the points of a Poisson point process with intensity measure m and moving independently of each other according to the law of some Gaussian process ξ. We describe all pairs (m, ξ) generating a s...

Spectral Representation of Stationary Processes

The spectral distribution measures the fraction of the total variance of the process γ(0) that can be attributed to a certain interval of frequencies.. If for example we have monthly data then one month corresponds to 2π, two month correspond to π, one year corresponds to π/6 . To measure the fraction of the variance generated by cyclical components of more than one year cycle length we would c...

Small Deviations of Smooth Stationary Gaussian Processes

We investigate the small deviation probabilities of a class of very smooth stationary Gaussian processes playing an important role in Bayesian statistical inference. Our calculations are based on the appropriate modification of the entropy method due to Kuelbs, Li, and Linde as well as on classical results about the entropy of classes of analytic functions. They also involve Tsirelson’s upper b...

Representations of Gaussian processes with stationary increments