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Título : Innovation Networks in the German Laser Industry : Evolutionary Change, Strategic Positioning, and Firm Innovativeness Tipo de documento: documento electrónico Autores: Kudic, Muhamed ; SpringerLink (Online service) Editorial: Cham : Springer International Publishing Fecha de publicación: 2015 Otro editor: Imprint: Springer Colección: Economic Complexity and Evolution, ISSN 2199-3173 Número de páginas: XX, 363 p. 43 illus Il.: online resource ISBN/ISSN/DL: 978-3-319-07935-6 Idioma : Inglés (eng) Palabras clave: Management Industrial management Economic geography Evolutionary economics organization policy sociology Economics R & D/Technology Policy Institutional/Evolutionary Innovation/Technology Organization Organizational Studies, Sociology Geography Clasificación: 658 Empresas. Organización de empresas Resumen: Technological innovation is fundamental to firm performance and economic prosperity. The aim of this book is to contribute to an in-depth understanding of collective innovation processes by analyzing publicly funded R&D cooperation and innovation networks in the German laser industry. Standing in a neo-Schumpeterian tradition, it employs interdisciplinary analytical concepts and draws upon a unique longitudinal dataset from the laser industry that covers more than two decades of observations. In brief, the book makes a valuable contribution by exploring how and why firm-specific R&D cooperation activities and network positions, large-scale network patterns, and evolutionary network change processes affect the innovative performance of laser source manufacturers in Germany Nota de contenido: Introduction and Theoretical Background: Introduction -- Theoretical Background -- Industry, Data and Methods: Laser Technology and the German Laser Industry -- Methodological Reflections and Data Sources -- Quantitative Concepts and Measures -- Dataset Design and Estimation Methods -- Descriptive Analysis: Industry Dynamics and Geographical Concentration -- Evolution of the Industry's Innovation Network -- Econometric Analysis: Causes and Consequences of Network Evolution -- Ego Networks and Firm Innovativeness -- Small World Patterns and Firm Innovativeness -- Network Positioning, Co-Location or Both? -- Summary, Conclusion and Outlook: Findings and Limitations -- Further Research and Conclusions En línea: http://dx.doi.org/10.1007/978-3-319-07935-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35428 Innovation Networks in the German Laser Industry : Evolutionary Change, Strategic Positioning, and Firm Innovativeness [documento electrónico] / Kudic, Muhamed ; SpringerLink (Online service) . - Cham : Springer International Publishing : Imprint: Springer, 2015 . - XX, 363 p. 43 illus : online resource. - (Economic Complexity and Evolution, ISSN 2199-3173) .
ISBN : 978-3-319-07935-6
Idioma : Inglés (eng)
Palabras clave: Management Industrial management Economic geography Evolutionary economics organization policy sociology Economics R & D/Technology Policy Institutional/Evolutionary Innovation/Technology Organization Organizational Studies, Sociology Geography Clasificación: 658 Empresas. Organización de empresas Resumen: Technological innovation is fundamental to firm performance and economic prosperity. The aim of this book is to contribute to an in-depth understanding of collective innovation processes by analyzing publicly funded R&D cooperation and innovation networks in the German laser industry. Standing in a neo-Schumpeterian tradition, it employs interdisciplinary analytical concepts and draws upon a unique longitudinal dataset from the laser industry that covers more than two decades of observations. In brief, the book makes a valuable contribution by exploring how and why firm-specific R&D cooperation activities and network positions, large-scale network patterns, and evolutionary network change processes affect the innovative performance of laser source manufacturers in Germany Nota de contenido: Introduction and Theoretical Background: Introduction -- Theoretical Background -- Industry, Data and Methods: Laser Technology and the German Laser Industry -- Methodological Reflections and Data Sources -- Quantitative Concepts and Measures -- Dataset Design and Estimation Methods -- Descriptive Analysis: Industry Dynamics and Geographical Concentration -- Evolution of the Industry's Innovation Network -- Econometric Analysis: Causes and Consequences of Network Evolution -- Ego Networks and Firm Innovativeness -- Small World Patterns and Firm Innovativeness -- Network Positioning, Co-Location or Both? -- Summary, Conclusion and Outlook: Findings and Limitations -- Further Research and Conclusions En línea: http://dx.doi.org/10.1007/978-3-319-07935-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35428 Ejemplares
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Título : Analysis of Phylogenetics and Evolution with R Tipo de documento: documento electrónico Autores: Emmanuel Paradis ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2012 Colección: Use R!, ISSN 2197-5736 Número de páginas: XIV, 386 p. 89 illus Il.: online resource ISBN/ISSN/DL: 978-1-4614-1743-9 Idioma : Inglés (eng) Palabras clave: Life sciences Bioinformatics Evolutionary biology Statistics Sciences Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completely updated, covering the full gamut of R packages for this area that have been introduced to the market since its previous publication five years ago. There is also a new chapter on the simulation of evolutionary data. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters Nota de contenido: Introduction -- First Steps in R for Phylogeneticists -- Phylogenetic Data in R -- Plotting Phylogenies -- Phylogeny Estimation -- Analysis of Macroevolution with Phylogenies -- Simulating Phylogenies and Evolutionary Data -- Developing and Implementing Phylogenetic Methods in R -- Short Course on Regular Expressions En línea: http://dx.doi.org/10.1007/978-1-4614-1743-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32769 Analysis of Phylogenetics and Evolution with R [documento electrónico] / Emmanuel Paradis ; SpringerLink (Online service) . - New York, NY : Springer New York, 2012 . - XIV, 386 p. 89 illus : online resource. - (Use R!, ISSN 2197-5736) .
ISBN : 978-1-4614-1743-9
Idioma : Inglés (eng)
Palabras clave: Life sciences Bioinformatics Evolutionary biology Statistics Sciences Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completely updated, covering the full gamut of R packages for this area that have been introduced to the market since its previous publication five years ago. There is also a new chapter on the simulation of evolutionary data. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters Nota de contenido: Introduction -- First Steps in R for Phylogeneticists -- Phylogenetic Data in R -- Plotting Phylogenies -- Phylogeny Estimation -- Analysis of Macroevolution with Phylogenies -- Simulating Phylogenies and Evolutionary Data -- Developing and Implementing Phylogenetic Methods in R -- Short Course on Regular Expressions En línea: http://dx.doi.org/10.1007/978-1-4614-1743-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32769 Ejemplares
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Título : Killer Cell Dynamics : Mathematical and Computational Approaches to Immunology Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Wodarz, Dominik Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Interdisciplinary Applied Mathematics, ISSN 0939-6047 num. 32 Número de páginas: XIII, 220 p Il.: online resource ISBN/ISSN/DL: 978-0-387-68733-9 Idioma : Inglés (eng) Palabras clave: Mathematics Immunology Cell biology Ecology Evolutionary Biomathematics Mathematical and Computational Biology Theoretical Ecology/Statistics Clasificación: 51 Matemáticas Resumen: This book reviews how mathematics can be used in combination with biological data in order to improve understanding of how the immune system works. This is illustrated largely in the context of viral infections. Mathematical models allow scientists to capture complex biological interactions in a clear mathematical language and to follow them to their precise logical conclusions. This can give rise to counter-intuitive insights which would not be attained by experiments alone, and can be used for the design of further experiments in order to address the mathematical results. This book provides both an introduction to the field of mathematical immunology, and an overview of many topics which are the subject of current research, covering a broad variety of immunological topics. It starts with basic principles of immunology and covers the dynamical interactions between the immune system and specific viral infections, including important human pathogens such as HIV. General biological and mathematical background material to both virus infection and immune system dynamics is provided, and each chapter begins with a simple introduction to the biological questions examined. This book is intended for an interdisciplinary audience. It explains the concept of mathematical modeling in immunology and shows how modeling has been used to address specific questions. It is intended both for the mathematical biologists who are interested in immunology, and for the biological readership that is interested in the use of mathematical models in immunology. Dominik Wodarz is an Associate Professor at the Department of Ecology and Evolutionary Biology at the University of California, Irvine Nota de contenido: Viruses and Immune Responses: A Dynamical View -- Models of CTL Responses and Correlates of Virus Control -- CTL Memory -- CD4 T Cell Help -- Immunodominance -- Multiple Infections and CTL Dynamics -- Control versus CTL-Induced Pathology -- Lytic versus Nonlytic Activity -- Dynamical Interactions between CTL and Antibody Responses -- Effector Molecules and CTL Homeostasis -- Virus-Induced Subversion of CTL Responses -- Boosting Immunity against Immunosuppressive Infections -- Evolutionary Aspects of Immunity En línea: http://dx.doi.org/10.1007/978-0-387-68733-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34509 Killer Cell Dynamics : Mathematical and Computational Approaches to Immunology [documento electrónico] / SpringerLink (Online service) ; Wodarz, Dominik . - New York, NY : Springer New York, 2007 . - XIII, 220 p : online resource. - (Interdisciplinary Applied Mathematics, ISSN 0939-6047; 32) .
ISBN : 978-0-387-68733-9
Idioma : Inglés (eng)
Palabras clave: Mathematics Immunology Cell biology Ecology Evolutionary Biomathematics Mathematical and Computational Biology Theoretical Ecology/Statistics Clasificación: 51 Matemáticas Resumen: This book reviews how mathematics can be used in combination with biological data in order to improve understanding of how the immune system works. This is illustrated largely in the context of viral infections. Mathematical models allow scientists to capture complex biological interactions in a clear mathematical language and to follow them to their precise logical conclusions. This can give rise to counter-intuitive insights which would not be attained by experiments alone, and can be used for the design of further experiments in order to address the mathematical results. This book provides both an introduction to the field of mathematical immunology, and an overview of many topics which are the subject of current research, covering a broad variety of immunological topics. It starts with basic principles of immunology and covers the dynamical interactions between the immune system and specific viral infections, including important human pathogens such as HIV. General biological and mathematical background material to both virus infection and immune system dynamics is provided, and each chapter begins with a simple introduction to the biological questions examined. This book is intended for an interdisciplinary audience. It explains the concept of mathematical modeling in immunology and shows how modeling has been used to address specific questions. It is intended both for the mathematical biologists who are interested in immunology, and for the biological readership that is interested in the use of mathematical models in immunology. Dominik Wodarz is an Associate Professor at the Department of Ecology and Evolutionary Biology at the University of California, Irvine Nota de contenido: Viruses and Immune Responses: A Dynamical View -- Models of CTL Responses and Correlates of Virus Control -- CTL Memory -- CD4 T Cell Help -- Immunodominance -- Multiple Infections and CTL Dynamics -- Control versus CTL-Induced Pathology -- Lytic versus Nonlytic Activity -- Dynamical Interactions between CTL and Antibody Responses -- Effector Molecules and CTL Homeostasis -- Virus-Induced Subversion of CTL Responses -- Boosting Immunity against Immunosuppressive Infections -- Evolutionary Aspects of Immunity En línea: http://dx.doi.org/10.1007/978-0-387-68733-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34509 Ejemplares
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Título : Quantitative Sociodynamics : Stochastic Methods and Models of Social Interaction Processes Tipo de documento: documento electrónico Autores: Helbing, Dirk ; SpringerLink (Online service) Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2010 Otro editor: Imprint: Springer Número de páginas: XXIX, 333 p Il.: online resource ISBN/ISSN/DL: 978-3-642-11546-2 Idioma : Inglés (eng) Palabras clave: Mathematics Operations research Decision making Game theory Probabilities Sociophysics Econophysics Social sciences Probability Theory and Stochastic Processes Operation Research/Decision Theory, Economics, Behav. Sciences Socio- Econophysics, Population Evolutionary Models Sciences, general Clasificación: 51 Matemáticas Resumen: This new edition of Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioral changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics and mathematics, but they have very often proven their explanatory power in chemistry, biology, economics and the social sciences as well. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces important concepts from nonlinear dynamics (e.g. synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches, a fundamental dynamic model is obtained, which opens new perspectives in the social sciences. It includes many established models as special cases, e.g. the logistic equation, the gravity model, some diffusion models, evolutionary game theory and social field theory. Moreover, it implies numerous new results and is relevant for various application areas, such as opinion formation, migration, the self-organization of behavioral conventions, and the behavior of customers and voters. Theoretical results are complemented and illustrated by numerous computer simulations. Quantitative Sociodynamics is relevant both for social scientists and natural scientists who are interested in the application of stochastic and synergetics concepts to interdisciplinary topics. Nota de contenido: and Summary -- Dynamic Decision Behavior -- Stochastic Methods and Non-linear Dynamics -- Master Equation in State Space -- Boltzmann-Like Equations -- Master Equation in Configuration Space -- The Fokker-Planck Equation -- Langevin Equations and Non-linear Dynamics -- Quantitative Models of Social Processes -- Problems and Terminology -- Decision Theoretical Specification of the Transition Rates -- Opinion Formation Models -- Social Fields and Social Forces -- Evolutionary Game Theory Game theory!evolutionary|bb -- Determination of the Model Parameters from Empirical Data Empirical experience|bb En línea: http://dx.doi.org/10.1007/978-3-642-11546-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33745 Quantitative Sociodynamics : Stochastic Methods and Models of Social Interaction Processes [documento electrónico] / Helbing, Dirk ; SpringerLink (Online service) . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010 . - XXIX, 333 p : online resource.
ISBN : 978-3-642-11546-2
Idioma : Inglés (eng)
Palabras clave: Mathematics Operations research Decision making Game theory Probabilities Sociophysics Econophysics Social sciences Probability Theory and Stochastic Processes Operation Research/Decision Theory, Economics, Behav. Sciences Socio- Econophysics, Population Evolutionary Models Sciences, general Clasificación: 51 Matemáticas Resumen: This new edition of Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioral changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics and mathematics, but they have very often proven their explanatory power in chemistry, biology, economics and the social sciences as well. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces important concepts from nonlinear dynamics (e.g. synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches, a fundamental dynamic model is obtained, which opens new perspectives in the social sciences. It includes many established models as special cases, e.g. the logistic equation, the gravity model, some diffusion models, evolutionary game theory and social field theory. Moreover, it implies numerous new results and is relevant for various application areas, such as opinion formation, migration, the self-organization of behavioral conventions, and the behavior of customers and voters. Theoretical results are complemented and illustrated by numerous computer simulations. Quantitative Sociodynamics is relevant both for social scientists and natural scientists who are interested in the application of stochastic and synergetics concepts to interdisciplinary topics. Nota de contenido: and Summary -- Dynamic Decision Behavior -- Stochastic Methods and Non-linear Dynamics -- Master Equation in State Space -- Boltzmann-Like Equations -- Master Equation in Configuration Space -- The Fokker-Planck Equation -- Langevin Equations and Non-linear Dynamics -- Quantitative Models of Social Processes -- Problems and Terminology -- Decision Theoretical Specification of the Transition Rates -- Opinion Formation Models -- Social Fields and Social Forces -- Evolutionary Game Theory Game theory!evolutionary|bb -- Determination of the Model Parameters from Empirical Data Empirical experience|bb En línea: http://dx.doi.org/10.1007/978-3-642-11546-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33745 Ejemplares
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Título : Statistical Methods in Molecular Evolution Tipo de documento: documento electrónico Autores: Nielsen, Rasmus ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XII, 505 p Il.: online resource ISBN/ISSN/DL: 978-0-387-27733-2 Idioma : Inglés (eng) Palabras clave: Life sciences Bioinformatics Evolutionary biology Plant genetics Biomathematics Statistics Sciences Biology for Sciences, Medicine, Health Genetics and Population Dynamics Mathematical Computational & Genomics Clasificación: 51 Matemáticas Resumen: In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders in the field and they will take the reader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book Nota de contenido: Markov Models in Molecular Evolution -- to Applications of the Likelihood Function in Molecular Evolution -- to Markov Chain Monte Carlo Methods in Molecular Evolution -- Population Genetics of Molecular Evolution -- Practical Approaches for Data Analysis -- Maximum Likelihood Methods for Detecting Adaptive Protein Evolution -- HyPhy: Hypothesis Testing Using Phylogenies -- Bayesian Analysis of Molecular Evolution Using MrBayes -- Estimation of Divergence Times from Molecular Sequence Data -- Models of Molecular Evolution -- Markov Models of Protein Sequence Evolution -- Models of Microsatellite Evolution -- Genome Rearrangement -- Phylogenetic Hidden Markov Models -- Inferences on Molecular Evolution -- The Evolutionary Causes and Consequences of Base Composition Variation -- Statistical Alignment: Recent Progress, New Applications, and Challenges -- Estimating Substitution Matrices -- Posterior Mapping and Posterior Predictive Distributions -- Assessing the Uncertainty in Phylogenetic Inference En línea: http://dx.doi.org/10.1007/0-387-27733-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35133 Statistical Methods in Molecular Evolution [documento electrónico] / Nielsen, Rasmus ; SpringerLink (Online service) . - New York, NY : Springer New York, 2005 . - XII, 505 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-0-387-27733-2
Idioma : Inglés (eng)
Palabras clave: Life sciences Bioinformatics Evolutionary biology Plant genetics Biomathematics Statistics Sciences Biology for Sciences, Medicine, Health Genetics and Population Dynamics Mathematical Computational & Genomics Clasificación: 51 Matemáticas Resumen: In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders in the field and they will take the reader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book Nota de contenido: Markov Models in Molecular Evolution -- to Applications of the Likelihood Function in Molecular Evolution -- to Markov Chain Monte Carlo Methods in Molecular Evolution -- Population Genetics of Molecular Evolution -- Practical Approaches for Data Analysis -- Maximum Likelihood Methods for Detecting Adaptive Protein Evolution -- HyPhy: Hypothesis Testing Using Phylogenies -- Bayesian Analysis of Molecular Evolution Using MrBayes -- Estimation of Divergence Times from Molecular Sequence Data -- Models of Molecular Evolution -- Markov Models of Protein Sequence Evolution -- Models of Microsatellite Evolution -- Genome Rearrangement -- Phylogenetic Hidden Markov Models -- Inferences on Molecular Evolution -- The Evolutionary Causes and Consequences of Base Composition Variation -- Statistical Alignment: Recent Progress, New Applications, and Challenges -- Estimating Substitution Matrices -- Posterior Mapping and Posterior Predictive Distributions -- Assessing the Uncertainty in Phylogenetic Inference En línea: http://dx.doi.org/10.1007/0-387-27733-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35133 Ejemplares
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