Bayesian Modeling In Bioinformatics
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Bayesian Modeling In Bioinformatics
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Author : Dipak K. Dey
language : en
Publisher: CRC Press
Release Date : 2010-09-03
Bayesian Modeling In Bioinformatics written by Dipak K. Dey and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-03 with Mathematics categories.
Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c
Case Studies In Bayesian Statistical Modelling And Analysis
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Author : Clair L. Alston
language : en
Publisher: John Wiley & Sons
Release Date : 2012-10-10
Case Studies In Bayesian Statistical Modelling And Analysis written by Clair L. Alston and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-10 with Mathematics categories.
Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.
Bayesian Network Technologies Applications And Graphical Models
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Author : Mittal, Ankush
language : en
Publisher: IGI Global
Release Date : 2007-03-31
Bayesian Network Technologies Applications And Graphical Models written by Mittal, Ankush and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-31 with Computers categories.
"This book provides an excellent, well-balanced collection of areas where Bayesian networks have been successfully applied; it describes the underlying concepts of Bayesian Networks with the help of diverse applications, and theories that prove Bayesian networks valid"--Provided by publisher.
Bayesian Thinking Modeling And Computation
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Author :
language : en
Publisher: Elsevier
Release Date : 2005-11-29
Bayesian Thinking Modeling And Computation written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-29 with Mathematics categories.
This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics
Bayesian Time Series Models
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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2011-08-11
Bayesian Time Series Models written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-11 with Computers categories.
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Mixed Effects Models For The Population Approach
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Author : Marc Lavielle
language : en
Publisher: CRC Press
Release Date : 2014-07-14
Mixed Effects Models For The Population Approach written by Marc Lavielle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-14 with Mathematics categories.
Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whol
Bayesian Modeling And Computation In Python
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Author : Osvaldo A. Martin
language : en
Publisher: CRC Press
Release Date : 2021-12-28
Bayesian Modeling And Computation In Python written by Osvaldo A. Martin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-28 with Computers categories.
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.
Essential Computing Skills For Biologists
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Author : Ziling Wang
language : en
Publisher: World Scientific
Release Date : 2018-07-02
Essential Computing Skills For Biologists written by Ziling Wang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-02 with Science categories.
This is a handbook of methods and protocols for biologists. It aimed at undergraduate, graduate students and researchers originally trained in biological or medical sciences who need to know how to access the data archives of genomes, proteins, metabolites, gene expression profiles and the questions these data and tools can answer. For each chapter, the conceptual and experimental background is provided, together with specific guidelines for handling raw data, including preprocessing and analysis.The content is structured into three parts. Part one introduces basic knowledge about popular bioinformatics tools, databases and web resources. Part two presents examples of omics bioinformatics applications. Part three provides basic statistical analysis skills and programming skills needed to handle and analyze omics datasets.
Inference Principles For Biostatisticians
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Author : Ian C. Marschner
language : en
Publisher: CRC Press
Release Date : 2014-12-11
Inference Principles For Biostatisticians written by Ian C. Marschner and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Mathematics categories.
Designed for students training to become biostatisticians as well as practicing biostatisticians, Inference Principles for Biostatisticians presents the theoretical and conceptual foundations of biostatistics. It covers the theoretical underpinnings essential to understanding subsequent core methodologies in the field.Drawing on his extensive exper
Probabilistic Graphical Models For Genetics Genomics And Postgenomics
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Author : Christine Sinoquet
language : en
Publisher: Oxford University Press, USA
Release Date : 2014
Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Christine Sinoquet and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Mathematics categories.
At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play.