Reconstructing probability distributions with Gaussian processes

Probability HITTING TIMES FOR GAUSSIAN PROCESSES

Palm distributions for log Gaussian Cox processes

This paper establishes a remarkable result regarding Palm distributions for a log Gaussian Cox process: the reduced Palm distribution for a log Gaussian Cox process is itself a log Gaussian Cox process which only differs from the original log Gaussian Cox process in the intensity function. This new result is used to study functional summaries for log Gaussian Cox processes.

Constant-Time Predictive Distributions for Gaussian Processes

One of the most compelling features of Gaussian process (GP) regression is its ability to provide well calibrated posterior distributions. Recent advances in inducing point methods have drastically sped up marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address this shortcoming...

Interpolation of Non-Gaussian Probability Distributions for Ensemble Visualization

A typical assumption is that ensemble data at each spatial location follows a Gaussian distribution. We investigate the consequences of that assumption when distributions are non-Gaussian. A sufficiently acceptable interpolation scheme needs to be devised for the interpolation of non-Gaussian distributions. We present two methods to calculate interpolations between two arbitrary distributions a...

Probability distributions for discrete one-dimensional coverage processes

Recursive decomposition of fundamental events in the directional, one-dimensional, discrete, stationary Boolean model has resulted in analytic representation of the event probabilities and the recursive representation of clump-length and gap-length densities. This paper applies the methodology to find analytic expressions for distributions of a number of random variables, including the number o...