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Of the two primary approaches to the classic source separation
problem, only one does not impose potentially unreasonable model
and likelihood constraints: the Bayesian statistical approach.
Bayesian methods incorporate the available information regarding
the model parameters and not only allow estimation of the sources
and mixing coefficients, but also allow inferences to be drawn from
them. Multivariate Bayesian Statistics: Models for Source
Separation and Signal Unmixing offers a thorough, self-contained
treatment of the source separation problem. After an introduction
to the problem using the "cocktail-party" analogy, Part I provides
the statistical background needed for the Bayesian source
separation model. Part II considers the instantaneous constant
mixing models, where the observed vectors and unobserved sources
are independent over time but allowed to be dependent within each
vector. Part III details more general models in which sources can
be delayed, mixing coefficients can change over time, and
observation and source vectors can be correlated over time. For
each model discussed, the author gives two distinct ways to
estimate the parameters. Real-world source separation problems,
encountered in disciplines from engineering and computer science to
economics and image processing, are more difficult than they
appear. This book furnishes the fundamental statistical material
and up-to-date research results that enable readers to understand
and apply Bayesian methods to help solve the many "cocktail party"
problems they may confront in practice.
Iron Acquisition by the Genus Mycobacterium summarizes the early
evidence for the necessity of iron in mycobacteria and the
discovery of the mycobacterial siderophores mycobactin,
carboxymycobactin, and exochelin. The structural characterization
of the mycobacterial siderophores is described. The genes so far
identified as essential for iron acquisition and maintenance of an
infection by pathogenic mycobacteria are discussed. The potential
role of siderocalin in iron gathering by M. tuberculosis is
featured. Because new drugs for M. tuberculosis are needed, this
brief also emphasizes the design of antibiotics that interfere with
siderophore biosynthesis and the use of siderophore analogs and/or
conjugates.
Emergency physicians assess and manage a wide variety of problems
from patients presenting with a diversity of severities, ranging
from mild to severe and life-threatening. They are expected to
maintain their competency and expertise in areas where there is
rapid knowledge change. Evidence-based Emergency Medicine is the
first book of its kind in emergency medicine to tackle the problems
practicing physicians encounter in the emergency setting using an
evidence-based approach. It summarizes the published evidence
available for the diagnosis and treatment of common emergency
health care problems in adults. Each chapter contextualizes a topic
area using a clinical vignette and generates a series of key
clinically important diagnostic and treatment questions. By
completing detailed reviews of diagnostic and treatment research,
using evidence from systematic reviews, RCTs, and prospective
observational studies, the authors provide conclusions and
practical recommendations. Focusing primarily on diagnosis in areas
where evidence for treatment is well accepted (e.g. DVTs), and
treatment in other diseases where diagnosis is not complex (e.g.
asthma), this text is written by leading emergency physicians at
the forefront of evidence-based medicine. Evidence-based Emergency
Medicine is ideal for emergency physicians and trainees, emergency
department staff, and family physicians specialising in the acute
care of medical and injured patients.
Brain imaging has revolutionised the field of Psychology - once
more concerned with IQ tests, reaction times and questionnaires.
Most Psychology departments now have access to an MRI scanner -
some have even renamed themselves as departments of cognitive
neuroscience. Yet brain imaging can be a minefield, whichever
discipline you approach it from. If you are a psychologist, you
will have been taught how to do behavioural experiments, but may
know little neuroanatomy or neurophysiology. If you are a
neurologist or psychiatrist, then you may know the neuroanatomy and
neurophysiology, but not know how to carry out experiments on
mental phenomena. This is a practical guide to brain imaging,
showing how it can advance a true neuroscience of human cognition.
It is accessible to those starting out in imaging, whilst also
informative for those who have already acquired some expertise. At
the heart of the book are 6 main chapters, focusing on - the
signal, experimental methods, anatomy, functional specialisation,
functional systems, and other methods. For students and researchers
in psychology and neuroscience, this is the essential companion
when embarking on brain imaging studies.
Virginia Polytechnic Institute, Engineering Experiment Station,
V31, No. 3, January, 1938.
Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.
Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the "cocktail-party" analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters.
Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many "cocktail party" problems they may confront in practice.
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