Model Based Inference In The Life Sciences Book PDF, EPUB Download & Read Online Free

Model Based Inference in the Life Sciences
Author: David R. Anderson
Publisher: Springer Science & Business Media
ISBN: 0387740759
Pages: 184
Year: 2007-12-22
View: 604
Read: 518
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.
Model Selection and Inference
Author: Kenneth P. Burnham, David R. Anderson
Publisher: Springer Science & Business Media
ISBN: 1475729170
Pages: 355
Year: 2013-11-11
View: 563
Read: 1283
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.
Intelligent Control in Drying
Author: Alex Martynenko, Andreas Bück
Publisher: CRC Press
ISBN: 0429811314
Pages: 454
Year: 2018-09-03
View: 1034
Read: 165
Despite the available general literature in intelligent control, there is a definite lack of knowledge and know-how in practical applications of intelligent control in drying. This book fills that gap. Intelligent Control in Drying serves as an innovative and practical guide for researchers and professionals in the field of drying technologies, providing an overview of control principles and systems used in drying operations, from classical to model-based to adaptive and optimal control. At the same time, it lays out approaches to synthesis of control systems, based on the objectives and control strategies, reflecting complexity of drying process and material under drying. This essential reference covers both fundamental and practical aspects of intelligent control, sensor fusion and dynamic optimization with respect to drying.
MRI in Psychiatry
Author: Christoph Mulert, Martha E. Shenton
Publisher: Springer
ISBN: 3642545424
Pages: 431
Year: 2014-06-27
View: 723
Read: 782
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.
When to Use What Research Design
Author: W. Paul Vogt, Dianne C. Gardner, Lynne M. Haeffele
Publisher: Guilford Press
ISBN: 1462503608
Pages: 378
Year: 2012-02-20
View: 280
Read: 583
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.
Likelihood-based Inference in Cointegrated Vector Autoregressive Models
Author: Søren Johansen
Publisher: Oxford University Press on Demand
ISBN: 0198774508
Pages: 267
Year: 1995
View: 383
Read: 220
This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.
Principles of Statistical Inference
Author: D. R. Cox
Publisher: Cambridge University Press
ISBN: 0521685672
Pages: 219
Year: 2006-08-10
View: 1219
Read: 468
A comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies.
Hierarchical Modeling and Inference in Ecology
Author: J. Andrew Royle, Robert M. Dorazio
Publisher: Elsevier
ISBN: 0080559255
Pages: 464
Year: 2008-10-15
View: 736
Read: 1108
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
Model-Based Reasoning in Science and Technology
Author: Lorenzo Magnani, Claudia Casadio
Publisher: Springer
ISBN: 3319389831
Pages: 678
Year: 2016-07-01
View: 918
Read: 1150
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.
Statistical Models Based on Counting Processes
Author: PER KRAGH ANDERSEN, Ornulf Borgan, Richard D. Gill, Niels Keiding
Publisher: Springer Science & Business Media
ISBN: 1461243483
Pages: 784
Year: 2012-12-06
View: 1152
Read: 436
Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, almost all the methods are given in concrete detail for use in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuarial mathematicians, reliability engineers and biologists). Much of the material has so far only been available in the journal literature (if at all), and so a wide variety of researchers will find this an invaluable survey of the subject.
Generalized Linear Models with Random Effects
Author: Youngjo Lee, John A. Nelder, Yudi Pawitan
Publisher: CRC Press
ISBN: 1420011340
Pages: 416
Year: 2006-07-13
View: 219
Read: 470
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.
Mathematics and Life Sciences
Author: Alexandra V. Antoniouk, Roderick V. N. Melnik
Publisher: Walter de Gruyter
ISBN: 3110288532
Pages: 328
Year: 2013-01-01
View: 671
Read: 761
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.
Information Criteria and Statistical Modeling
Author: Sadanori Konishi, Genshiro Kitagawa
Publisher: Springer Science & Business Media
ISBN: 0387718877
Pages: 276
Year: 2007-09-12
View: 841
Read: 579
Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.
Experimental Ecology
Author: William J. Resetarits, Joseph Bernardo
Publisher: Oxford University Press on Demand
ISBN: 0195150422
Pages: 470
Year: 1998
View: 584
Read: 484
Experimentation is now a dominant approach in contemporary ecological research, pervading studies at all levels of biological organization and across diverse taxa and habitats. In this volume eminent ecologists discuss and evaluate the full range of experimental approaches, from laboratory microcosms to manipulation of entire ecosystems.
Data Analysis for the Life Sciences with R
Author: Rafael A. Irizarry, Michael I. Love
Publisher: CRC Press
ISBN: 1498775861
Pages: 376
Year: 2016-10-04
View: 369
Read: 395
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.