Signal Processing - July 2017 - 78

Table 15. The results of AMIS [21] (GP example).
MSE

1.32

1.35

1.26

1.27

1.23

200

100

200

500

1,000

S = TM = 10

Range

MMSE = 1.23 ___________ Maximum MSE = 1.35

Table 16. The results of LAIS [23] (GP example).
MSE

1.04

0.46

0.21

0.09

0.03

0.31

0.65

100

500

1,000

5,000

1,000

500

100

NT (M + 1) = 10 4

Range

MMSE = 0.03 ___________ Maximum MSE = 1.04

AIS in deep learning for computing the weights of the hidden layers. The addressed problems will not only require estimating unknown quantities but also finding the best models
from a set of predefined models or finding the best model in
nonparametric Bayesian settings where the number of models
is not set a priori.

Acknowledgments
We gratefully acknowledge the National Science Foundation
under awards CCF-1617986 (Mónica F. Bugallo) and CCF1618999 (Petar M. Djuric´); Ministerio de Economía y
Competitividad of Spain under TEC2015-69868-C2-1-R
ADVENTURE and the Office of Naval Research Global
under award N62909-15-1-2011 (Joaquín Míguez); the
European Research Council (ERC) through the ERC
Consolidator Grant SEDAL ERC-2014-CoG 647423
(Luca Martino); MINECO of Spain under TEC2015-64835C3-3-R MIMOD-PLC project, Ministerio de Educación,
Cultura y Deporte of Spain under CAS15/00350 grant, and
Universidad Politécnica de Madrid through a mobility grant
for a short visit to Stony Brook University (David Luengo)
and MINECO of Spain through Red de Excelencia KERMES
TEC2016-81900-REDT (David Luengo and Luca Martino).

Authors
Mónica F. Bugallo (monica.bugallo@stonybrook.edu)
received her B.S., M.S., and Ph.D. degrees in computer science and engineering from the University of A Coruña, Spain.
She is an associate professor of electrical and computer engineering and the faculty director of the Women in Science and
Engineering program at Stony Brook University, New York.
Her research interests are in the field of statistical signal pro78

cessing, with emphasis on the theory of Monte Carlo methods
and their application to different disciplines including biomedicine, sensor networks, and finance. She has authored and
coauthored two book chapters and more than 150 journal
papers and refereed conference articles. She is a Senior
Member of the IEEE.
Víctor Elvira (victor.elvira@imt-lille-douai.fr) received his
B.S., M.S., and Ph.D. degrees in electrical engineering from
the Universidad de Cantabria, Spain, in 2007, 2008, and 2011,
respectively. Currently, he is with IMT Lille Douai, Villeneuve
d'Ascq, France, and Université de Lille and CRIStAL
Laboratory (UMR 9189), Villeneuve d'Ascq, France. He is a
Member of the IEEE.
Luca Martino (luca.martino@uv.es) received his M.S.
degree in electronic engineering from the Politecnico di
Milano, Italy, and his Ph.D. degree in statistical signal processing in 2011 from the Universidad Carlos III de Madrid,
Spain. Currently, he is with the Image Processing Laboratory,
Universitat de València, Spain.
David Luengo (david.luengo@upm.es) received his M.S.
and Ph.D. degrees in electrical engineering from the
Universidad de Cantabria, Spain, in 1998 and 2006, respectively. From 2003 to 2011, he was an assistant professor with
the Universidad Carlos III de Madrid, Spain. Since 2011, he
has been an associate professor at the Universidad Politécnica
de Madrid. His research interests include statistical signal
processing, Monte Carlo methods, and multitask learning. He
is a Member of the IEEE.
Joaquín Míguez (joaquin.miguez@uc3m.es) received his
M.S. and Ph.D. degrees in computer engineering from the
University of A Coruña, Spain, in 1997 and 2000, respectively. Currently, he is with the department of signal theory and
communications, Universidad Carlos III de Madrid. His
research interests are in the fields of applied probability, statistical signal processing, Bayesian analysis, dynamical systems
and the theory and applications of Monte Carlo methods. He
is a corecipient of the 2007 IEEE Signal Processing Magazine
Best Paper Award.
Petar M. Djuric´ (petar.djuric@stonybrook.edu) received
his B.S. and M.S. degrees in electrical engineering from the
University of Belgrade, Serbia, and his Ph.D. degree in electrical engineering from the University of Rhode Island. He is
currently a distinguished professor in the Department of
Electrical and Computer Engineering at Stony Brook
University, New York. His research has been in signal and
information processing, with an emphasis on Monte Carlobased methods, signal processing over networks and applications in wireless sensor networks, and radio-frequency
identification. He received the IEEE Signal Processing
Magazine Best Paper Award in 2007 and the EURASIP
Technical Achievement Award in 2012. He is a Fellow of the
IEEE and EURASIP.

References

[1] X. Sheng and Y.-H. Hu, "Maximum likelihood multiple-source localization
using acoustic energy measurements with wireless sensor networks," IEEE Trans.
Signal Process., vol. 53, no. 1, pp. 44-53, 2005.

IEEE SIGNAL PROCESSING MAGAZINE

July 2017

Table of Contents for the Digital Edition of Signal Processing - July 2017