Search results for "Estimation theory"

Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference

2018

This letter introduces warped Gaussian process (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such a prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more a…

Randomized kernels for large scale Earth observation applications

2020

Abstract Current remote sensing applications of bio-geophysical parameter estimation and image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. New satellite sensors involving a high number of improved time, space and wavelength resolutions give rise to challenging computational problems. Standard physical inversion techniques cannot cope efficiently with this new scenario. Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has greatly helped in the last years to develop st…

Futures pricing in electricity markets based on stable CARMA spot models

2012

We present a new model for the electricity spot price dynamics, which is able to capture seasonality, low-frequency dynamics and the extreme spikes in the market. Instead of the usual purely deterministic trend we introduce a non-stationary independent increments process for the low-frequency dynamics, and model the large uctuations by a non-Gaussian stable CARMA process. The model allows for analytic futures prices, and we apply these to model and estimate the whole market consistently. Besides standard parameter estimation, an estimation procedure is suggested, where we t the non-stationary trend using futures data with long time until delivery, and a robust L 1 -lter to nd the states of …

Nowcasting COVID‐19 incidence indicators during the Italian first outbreak

2020

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replica…

Improvement in MRS parameter estimation by joint and laterally constrained inversion of MRS and TEM data

2012

We developed a new scheme for joint and laterally constrained inversion (LCI) of magnetic resonance sounding (MRS) data and transient electromagnetic (TEM) data, which greatly improves the estimation of the MRS model parameters. During the last few decades, electrical and electromagnetic methods have been widely used for groundwater investigation, but they suffer from some inherent limitations; for example, equivalent layer sequences. Furthermore, the water content information is only empirically correlated to resistivity of the formation. MRS is a noninvasive geophysical technique that directly quantifies the water content distribution from surface measurements. The resistivity informatio…

Biophysical parameter estimation with adaptive Gaussian Processes

2009

We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of ea…

An iterative based approach for hysteresis parameters estimation in Magnetorheological dampers

2012

The following work entails the problem of regenerating the hysteresis loop in the Magnetorheological (MR) dampers. The collected data from tests are not sufficient neither efficient for designing optimal controls compensating for the hysteresis in the dampers. This work presents an iterative based approach for estimating the hysteresis parameters, the method however can be generalized for different kind of dampers or actuators hence the hysteresis loop can be generalized using available test data. Some assumptions can be introduced in order to facilitate the underlines of the parameters estimation, one of the assumptions in this work is to use predetermined hysteresis parameters and regener…

Reliability of Parameter Estimation Methods Applied to the Identification of Biomedical Multicompartment Systems

1985

Abstract The reliability of parameter estimation in biomedical multicompartment models is influenced by the chosen mathematical representation of the model, by the sensitivity of the output on the relevant parameters to be identified and by the experimental conditions, e.g. choice of the data-set. this paper the reliability of different parameter estimation methods applied in a multi-compartment model of the circulatory system has been studied under the assumption that there exists a partially system output insensitivity against the compliance parameters. Suitable conditions for efficient parameter estimation have been found by simulation of sensitivity equations of the system, which is of …

2014

This paper considers the parameter estimation for linear time-invariant (LTI) systems in an input-output setting with output error (OE) time-delay model structure. The problem of missing data is commonly experienced in industry due to irregular sampling, sensor failure, data deletion in data preprocessing, network transmission fault, and so forth; to deal with the identification of LTI systems with time-delay in incomplete-data problem, the generalized expectation-maximization (GEM) algorithm is adopted to estimate the model parameters and the time-delay simultaneously. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

Parameter identification of linear induction motor model in extended range of operation by means of input-output data

2012

This paper proposes a technique for the off-line estimation of the electrical parameters of the equivalent circuit of linear induction machines (LIM), taking into consideration the end effects, and focuses on the application of an algorithm based on the minimization of a suitable cost function involving the differences of measured and computed by simulation inductor current components. This method exploits an entire start-up transient of the LIM to estimate all the 4 electrical parameters of the machine (Rs, L s, σ Ls, Tr). It proposes also a set of tests to be made to estimate the variation of the magnetic parameters of the LIM versus the magnetizing current as well as the magnetizing curv…