Search results for "Gauss"
showing 10 items of 701 documents
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
2021
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …
Spatio-temporal log-Gaussian Cox processes on earthquake events
2016
This work presents an application of spatio-temporal log-Gaussian Cox processes for the description of earthquake events. To explain the overall spatial trend, spatial geological information in the study area such as faults and volcanoes are introduced in the model. Moreover, an anisotropic specification of the covariance matrix of the Gaussian process is used to improve the explanation of the phenomenon. We apply and compare different models to explain the seismic events occurred in Alaska over the last decades.
Local Spatio-Temporal Log-Gaussian Cox Processes for seismic data analysis
2022
We propose a local version of the spatio-temporal log-Gaussian Cox processes (LGCPs) employing the Local Indicators of Spatio-Temporal Association (LISTA) functions into the minimum contrast procedure to obtain space as well as time-varying parameters. We resort to the joint minimum contrast method fitting method to estimate the set of second-order parameters for the class of Spatio-Temporal LGCPs. We employ the proposed methodology to analyse real seismic data occurred Greece between 2004 and 2015.
The effect of round-off error on long memory processes
2011
We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequently, we find that the discretized process is also long memory with the same Hurst exponent as the original process. We consider the properties of two estimators of the Hurst exponent, namely the local Whittle (LW) estimator and the Detrended Fluctuation Analysis (DFA). By using analytical considerations and numerical simulations we show that, in presence of round-off error, both…
Special functions for the study of economic dynamics: The case of the Lucas-Uzawa model
2008
The special functions are intensively used in mathematical physics to solve differential systems. We argue that they should be most useful in economic dynamics, notably in the assessment of the transition dynamics of endogenous economic growth models. We illustrate our argument on the famous Lucas-Uzawa model, which we solve by the means of Gaussian hypergeometric functions. We show how the use of Gaussian hypergeometric functions allows for an explicit representation of the equilibrium dynamics of all variables in level. The parameters of the involved hypergeometric functions are identified using the Pontryagin conditions arising from the underlying optimization problems. In contrast to th…
Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance
2009
The main purpose of this paper is to present a theoretically sound portfolio performance measure that takes into account higher moments of the distribution of returns. First, we perform a study of the investor's preferences to higher moments of distribution within expected utility theory and discuss the performance measurement. To illustrate the investor's preferences to higher moments and the computation of a performance measure, we provide an approximation analysis of the optimal capital allocation problem and derive a formula for the Sharpe ratio adjusted for skewness of distribution. This performance measure justifies the notion of the Generalized Sharpe Ratio (GSR) introduced by Hodges…
Modeling Term Structure Dynamics in the Nordic Electricity Swap Market
2010
We analyze the daily returns of Nordic electricity swaps and identify significant risk premia in the short end of the market. On average, long positions in this part of the swap market yield negative returns. The daily returns are distinctively non-normal in terms of tail-fatness, but we find little evidence of asymmetry. We investigate if the flexible four-parameter class of normal inverse Gaussian (NIG) distributions can capture the observed stylized facts and find that this class of distributions offers a remarkably improved fit relative to the normal distribution. We also compare the fit with that of the four-parameter class of stable distributions; the NIG law outperforms the stable la…
Improved fast Gauss transform for meshfree electromagnetic transients simulations
2019
Abstract In this paper improved fast summations are introduced to enhance a meshfree solver for the evolution of the electromagnetic fields over time. The original method discretizes the time-domain Maxwell’s curl equations via Smoothed Particle Hydrodynamics requiring many summations on the first derivatives of the kernel function and field vectors at each time step. The improved fast Gauss transform is properly adopted picking up the computational cost and the memory requirement at an acceptable level preserving the accuracy of the computation. Numerical simulations in two-dimensional domains are discussed giving evidence of improvements in the computation compared to the standard formula…
Monotonically convergent optimal control theory of quantum systems under a nonlinear interaction with the control field
2008
We consider the optimal control of quantum systems interacting non-linearly with an electromagnetic field. We propose new monotonically convergent algorithms to solve the optimal equations. The monotonic behavior of the algorithm is ensured by a non-standard choice of the cost which is not quadratic in the field. These algorithms can be constructed for pure and mixed-state quantum systems. The efficiency of the method is shown numerically on molecular orientation with a non-linearity of order 3 in the field. Discretizing the amplitude and the phase of the Fourier transform of the optimal field, we show that the optimal solution can be well-approximated by pulses that could be implemented ex…
Beam test results of IHEP-NDL Low Gain Avalanche Detectors(LGAD)
2020
A High-Granularity Timing Detector (HGTD) is proposed based on the Low-Gain Avalanche Detector (LGAD) for the ATLAS experiment to satisfy the time resolution requirement for the up-coming High Luminosity at LHC (HL-LHC). We report on beam test results for two proto-types LGADs (BV60 and BV170) developed for the HGTD. Such modules were manufactured by the Institute of High Energy Physics (IHEP) of Chinese Academy of Sciences (CAS) collaborated with Novel Device Laboratory (NDL) of the Beijing Normal University. The beam tests were performed with 5 GeV electron beam at DESY. The timing performance of the LGADs was compared to a trigger counter consisting of a quartz bar coupled to a SiPM read…