Statistical analysis of data from acoustic tags: Methods for combining data streams and modeling animal behavior

Abstract. Data assimilation methods provide a rigorous statistical framework for constraining the parametric uncertainty of land surface models (LSMs), with the aim of improving our predictive capability as well as identifying areas in which the models need improvement. The increase in the number of available datasets in recent years allows us to address different aspects of the model at a variety of spatial and temporal scales. However, combining data streams in a DA system is not a trivial task. In this study we highlight some of the challenges surrounding multiple data stream assimilation, with a particular focus on the carbon cycle component of LSMs. We examine the impact of biases and inconsistencies between the observations and the model (resulting in non Gaussian error distributions) and the impact of non-linearity in model dynamics. In addition we explore the differences between performing a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data steam is assimilated sequentially), given the assumptions inherent to the inversion algorithm chosen for this study. We demonstrate some of these issues by assimilating synthetic observations into two simple models: the first a simplified version of the carbon cycle processes represented in many LSMs, and the second a non-linear toy model. We further discuss these experimental results in the context of recent studies in the carbon cycle data assimilation literature, and finally we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their models.

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Combining data streams of doubtful provenance

10.1142/9789811242380_0017 ◽

2022 ◽

pp. 285-305

Author(s):

Kavya Jagan ◽

Alistair B. Forbes

Keyword(s):

Data Streams ◽

Combining Data

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010013064x ◽

1992 ◽

Vol 50 (2) ◽

pp. 1202-1203

Author(s):

Gianluigi Botton ◽

Gilles L'espérance

Keyword(s):

Statistical Analysis ◽

Spatial Resolution ◽

Personal Computer ◽

Systematic Study ◽

Present Author ◽

Chemical Elements ◽

Detection Limits ◽

Research Groups ◽

Quantitative Performance ◽

Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

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