12,562 research outputs found
A perspective on gaussian processes for earth observation
Earth observation (EO) by airborne and satellite remote sensing and in-situ
observations play a fundamental role in monitoring our planet. In the last
decade, machine learning and Gaussian processes (GPs) in particular has
attained outstanding results in the estimation of bio-geo-physical variables
from the acquired images at local and global scales in a time-resolved manner.
GPs provide not only accurate estimates but also principled uncertainty
estimates for the predictions, can easily accommodate multimodal data coming
from different sensors and from multitemporal acquisitions, allow the
introduction of physical knowledge, and a formal treatment of uncertainty
quantification and error propagation. Despite great advances in forward and
inverse modelling, GP models still have to face important challenges that are
revised in this perspective paper. GP models should evolve towards data-driven
physics-aware models that respect signal characteristics, be consistent with
elementary laws of physics, and move from pure regression to observational
causal inference.Comment: 1 figur
Mandelbrot's 1/f fractional renewal models of 1963-67: The non-ergodic missing link between change points and long range dependence
The problem of 1/f noise has been with us for about a century. Because it is
so often framed in Fourier spectral language, the most famous solutions have
tended to be the stationary long range dependent (LRD) models such as
Mandelbrot's fractional Gaussian noise. In view of the increasing importance to
physics of non-ergodic fractional renewal models, I present preliminary results
of my research into the history of Mandelbrot's very little known work in that
area from 1963-67. I speculate about how the lack of awareness of this work in
the physics and statistics communities may have affected the development of
complexity science, and I discuss the differences between the Hurst effect, 1/f
noise and LRD, concepts which are often treated as equivalent.Comment: 11 pages. Corrected and improved version of a manuscript submitted to
ITISE 2016 meeting in Granada, Spai
Inferring the intensity of Poisson processes at the limit of the detector sensitivity (with a case study on gravitational wave burst search)
We consider the issue of reporting the result of search experiment in the
most unbiased and efficient way, i.e. in a way which allows an easy
interpretation and combination of results and which do not depend on whether
the experimenters believe or not to having found the searched-for effect. Since
this work uses the language of Bayesian theory, to which most physicists are
not used, we find that it could be useful to practitioners to have in a single
paper a simple presentation of Bayesian inference, together with an example of
application of it in search of rare processes.Comment: 36 pages, 11 figures, Latex files using cernart.cls (included). This
paper and related work are also available at
http://www-zeus.roma1.infn.it/~agostini/prob+stat.htm
A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA
Long memory plays an important role in many fields by determining the
behaviour and predictability of systems; for instance, climate, hydrology,
finance, networks and DNA sequencing. In particular, it is important to test if
a process is exhibiting long memory since that impacts the accuracy and
confidence with which one may predict future events on the basis of a small
amount of historical data. A major force in the development and study of long
memory was the late Benoit B. Mandelbrot. Here we discuss the original
motivation of the development of long memory and Mandelbrot's influence on this
fascinating field. We will also elucidate the sometimes contrasting approaches
to long memory in different scientific communitiesComment: 40 page
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
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