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 of 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 Romer 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. From the reviews: "...Overall this is a very useful book in an area of increasing importance. " Journal of the Royal Statistical Society "I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006 "Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006 "Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological resear
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这本书的封面设计得相当专业,那种深沉的蓝色调,配上严谨的字体排版,立刻让人感受到它内容的深度与广度。我本来是带着一种既期待又有些畏惧的心情翻开它的。作为一名对生物学基础尚算了解,但对统计学应用领域还比较生疏的研究者,我最关心的就是它的入门友好度。坦白说,前几章在铺陈基础概念时,处理得非常扎实,没有急于跳入复杂的公式推导,而是先用清晰的语言勾勒出分子进化研究中遇到的核心问题,比如如何量化亲缘关系、如何估计突变速率。这种循序渐进的构建方式,极大地降低了初学者的心理门槛。它不像某些教科书那样,上来就抛出一堆符号和假设,让人望而却步。相反,它更像是邀请你一起走进一个逻辑缜密的花园,每一步都有明确的指引,让你能稳健地建立起对整个学科框架的认知。对于那些希望系统性地学习分子进化统计学的人来说,这种详实的铺垫无疑是至关重要的第一步,它保证了后续学习的连贯性和理解的深度,让人愿意沉下心来细细品味每一个章节的精妙之处。
评分深入到中间部分,这本书的真正价值才开始显现出来,尤其是在处理**模型选择与假设检验**的章节。我必须承认,这部分内容的处理方式极其精妙且具有极强的实操指导意义。作者并没有满足于仅仅罗列已有的进化模型,而是花了大量篇幅去剖析构建这些模型的底层逻辑——为什么我们选择某个特定的泊松过程或者卡方检验,其背后的生物学假设是什么,以及更关键的,**当这些假设被违反时,我们该如何应对**。书中对最大似然法(Maximum Likelihood)的阐述,简直是教科书级别的典范。它不只是展示了如何计算似然函数,更重要的是,它通过一系列巧妙的案例,展示了如何在实际的基因序列数据中,动态地调整参数估计的稳健性。这种对“模型局限性”的坦诚和深入探讨,远超一般教材的范畴,它培养的是一种批判性的思维,而不是死记硬背的操作指南。对于那些需要设计原创性进化实验的研究人员来说,这种对统计工具背后的哲学思考,是无价之宝。
评分最后,从**作为参考书的耐用性**来看,这本书也展现出了极高的价值。它不是那种读完一遍就束之高阁的“流行读物”,而是更倾向于可以反复查阅的工具书。尤其值得一提的是,书末的参考文献和索引部分编排得极其详尽和准确。每当我在分析某个特定的数据集,对某个模型参数的理论依据产生疑问时,都能迅速地追溯到其原始出处或者相关的章节进行深入回顾。这种“可追溯性”在学术研究中至关重要。它意味着这本书不仅传授了知识,更搭建了一个通往更深层学术殿堂的索引系统。随着我未来研究的深入,可以预见,我将需要一遍又一遍地回归到这本书中,去校准我的方法论,去验证我的统计假设。它已经不仅仅是一本书,更像是一个可靠的、时刻待命的同行顾问。
评分一个常常被忽略,但这本书却着重强调的亮点在于其**跨学科的视野**。它不仅仅局限于传统的分子生物学范畴,而是巧妙地将信息论、随机过程理论甚至少量的时间序列分析方法引入进来,用以解决分子进化中的特有难题。特别是关于**基因组尺度数据的变异性建模**那几章,作者展现了惊人的跨界整合能力。他们并没有将统计方法视为孤立的数学工具,而是将其置于一个更宏大的、处理海量生物学数据的背景下来讨论。例如,在讨论如何处理高通量测序数据中特有的技术误差时,书中提出的混合模型,就融合了来自不同统计学分支的精髓。这对于希望推动自身研究进入“大数据”时代的生物学家来说,具有极强的启发性。它告诉我,要真正理解分子进化的复杂性,就必须跳出单一学科的藩篱,拥抱更广阔的统计学工具箱。
评分这本书的排版和图表质量也值得称赞,这对于一本涉及大量数学推导的专业书籍来说,是一个不小的挑战。清晰的图表是理解复杂统计关系的桥梁,而这本书在这方面做得非常出色。例如,在讨论**贝叶斯推断在系统发育重构中的应用**时,作者插入的那些流程图和马尔可夫链蒙特卡洛(MCMC)的收敛诊断图示,直观到令人拍案叫绝。我过去在其他资料中看到的MCMC图常常模棱两可,但这里的图例似乎经过了精心优化,每一个坐标轴的含义、每一个参数的后验分布变化趋势,都标注得一清二楚。这使得那些原本需要花费大量时间在脑海中进行空间想象的抽象概念,瞬间变得具体、可触摸。可以说,对于那些希望将前沿的计算方法应用于实际数据分析的学者而言,这本书提供的视觉辅助,极大地缩短了从理论到实践的转化周期。这种对细节的关注,体现了作者对读者学习体验的深切尊重。
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