Parameter estimation for Vasicek model driven by a general Gaussian noise

Simplified noise model parameter estimation for signal-dependent noise

In this paper, we present a noise parameter estimation method using a simplified signaldependent noise model. The generic Poisson–Gaussian noise model is simplified to a Gaussian–Gaussian noise model. From the simplified noise model, we experimentally verify that the value obtained by the robust median estimator is almost the same as the mean of the noise standard deviation. Based on this prope...

Parameter estimation: known vector signals in unknown Gaussian noise

Abstract This paper develops recursive, convergent estimators for the parameters of finite Gaussian mixtures with a common covariance matrix. The mean vectors (signals) of the component densities are assumed to be known. The motivation for the study stems from digital communication. The basic approach is first illustrated for the case of an independent identically distributed sequence of sample...

Quantum parameter estimation using general single-mode Gaussian states

Parameter Estimation of a Known Chaotic Time Series Corrupted by White Gaussian Noise

The subject of parameter estimation in linear signals embedded in white Gaussian noise has been extensively studied. The subject of nonlinear choatic signals, however, has received much less attention. This paper will examine some of the known techniques for estimating the parameters of chaotic signals such as iterative maps, including the tent map and the logistic map.

Machine Learning Vasicek Model Calibration with Gaussian Processes

In this paper we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The ma...