Download model based inference in the life sciences in pdf or read model based inference in the life sciences in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get model based inference in the life sciences in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Model Based Inference In The Life Sciences

Author: David R. Anderson
Publisher: Springer Science & Business Media
ISBN: 9780387740751
Size: 10.65 MB
Format: PDF, Kindle
View: 3434
Download and Read
This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.

Mri In Psychiatry

Author: Christoph Mulert
Publisher: Springer
ISBN: 3642545424
Size: 35.87 MB
Format: PDF, Kindle
View: 306
Download and Read
This is the first comprehensive textbook on the use of MRI in psychiatry covering imaging techniques, brain systems and a review of findings in different psychiatric disorders. The book is divided into three sections, the first of which covers in detail all the major MRI-based methodological approaches available today, including fMRI, EEG-fMRI, DTI and MR spectroscopy. In addition, the role of MRI in imaging genetics and combined brain stimulation and imaging is carefully explained. The second section provides an overview of the different brain systems that are relevant for psychiatric disorders, including the systems for perception, emotion, cognition and reward. The final part of the book presents the MRI findings that are obtained in all the major psychiatric disorders using the previously discussed techniques. Numerous carefully chosen images support the informative text, making this an ideal reference work for all practitioners and trainees with an interest in this flourishing field.

Mathematics And Life Sciences

Author: Alexandra V. Antoniouk
Publisher: Walter de Gruyter
ISBN: 3110288532
Size: 40.13 MB
Format: PDF, Mobi
View: 4218
Download and Read
The book provides a unique collection of in-depth mathematical, statistical, and modeling methods and techniques for life sciences, as well as their applications in a number of areas within life sciences. It also includes a range of new ideas that represent emerging frontiers in life sciences where the application of such quantitative methods and techniques is becoming increasingly important. The book is aimed at researchers in academia, practitioners and graduate students who want to foster interdisciplinary collaborations required to meet the challenges at the interface of modern life sciences and mathematics.

Issues In Life Sciences Molecular Biology 2011 Edition

Author:
Publisher: ScholarlyEditions
ISBN: 1464963487
Size: 50.75 MB
Format: PDF, ePub, Mobi
View: 3588
Download and Read
Issues in Life Sciences: Molecular Biology / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Life Sciences—Molecular Biology. The editors have built Issues in Life Sciences: Molecular Biology: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Life Sciences—Molecular Biology in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Life Sciences: Molecular Biology: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Model Selection And Inference

Author: Kenneth P. Burnham
Publisher: Springer Science & Business Media
ISBN: 1475729170
Size: 41.16 MB
Format: PDF, Mobi
View: 313
Download and Read
Statisticians and applied scientists must often select a model to fit empirical data. This book discusses the philosophy and strategy of selecting such a model using the information theory approach pioneered by Hirotugu Akaike. This approach focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book includes practical applications in biology and environmental science.

When To Use What Research Design

Author: W. Paul Vogt
Publisher: Guilford Press
ISBN: 1462503608
Size: 36.92 MB
Format: PDF, Mobi
View: 6692
Download and Read
Systematic, practical, and accessible, this is the first book to focus on finding the most defensible design for a particular research question. Thoughtful guidelines are provided for weighing the advantages and disadvantages of various methods, including qualitative, quantitative, and mixed methods designs. The book can be read sequentially or readers can dip into chapters on specific stages of research (basic design choices, selecting and sampling participants, addressing ethical issues) or data collection methods (surveys, interviews, experiments, observations, archival studies, and combined methods). Many chapter headings and subheadings are written as questions, helping readers quickly find the answers they need to make informed choices that will affect the later analysis and interpretation of their data. Useful features include: *Easy-to-navigate part and chapter structure. *Engaging research examples from a variety of fields. *End-of-chapter tables that summarize the main points covered. *Detailed suggestions for further reading at the end of each chapter. *Integration of data collection, sampling, and research ethics in one volume. *Comprehensive glossary.

A Mathematical Primer For Social Statistics

Author: John Fox
Publisher: SAGE
ISBN: 1412960800
Size: 34.44 MB
Format: PDF, ePub
View: 550
Download and Read
Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.

Hierarchical Modeling And Inference In Ecology

Author: J. Andrew Royle
Publisher: Elsevier
ISBN: 0080559255
Size: 13.12 MB
Format: PDF, Docs
View: 6884
Download and Read
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site

Principles Of Statistical Inference

Author: D. R. Cox
Publisher: Cambridge University Press
ISBN: 0521685672
Size: 29.20 MB
Format: PDF
View: 1109
Download and Read
A comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies.

Model Based Reasoning In Science And Technology

Author: Lorenzo Magnani
Publisher: Springer
ISBN: 3319389831
Size: 29.53 MB
Format: PDF, Kindle
View: 5975
Download and Read
This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning.