Gaussian processes offer a versatile framework to model and analyse continuous random phenomena, making them particularly useful in quantifying the probability of ruin in financial and insurance ...
The Annals of Probability, Vol. 1, No. 6 (Dec., 1973), pp. 968-981 (14 pages) This paper is mainly a survey of results on the problem of finding necessary and sufficient conditions for a Gaussian ...
This paper presents a new approach to the analysis of mixed processes $X_{t}=B_{t}+G_{t},\qquad t\in[0,T],$ where Bt is a Brownian motion and Gt is an independent ...
This video presents an uncertainty estimation algorithm for planetary exploration aerial robots - ARDEA. The robot detects rovers and landers semantically. The key technology behind is a sparse ...
Herlands, William, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. "Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data." Proceedings of Machine Learning ...
All sorts of physical processes in this analog world exhibit some degree of randomness. Think of noise, for example. Many noisy processes are described by Gaussian probability distributions. We should ...
On Friday the 2nd of September 2022, M.Sc. Krista Longi defends her doctoral thesis on Gaussian Processes and Convolutional Neural Networks for Modeling Sensor Data. The thesis is related to research ...
The class of Markov-functional models (MFMs) provides a framework that can be used to define interest-rate models of finite dimension calibrated to any arbitrage-free formula for caplet or swaption ...
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