An Introduction to Probability Theory: One Random Variable An Introduction to Probability Theory: Many Random Variables Statistics: An Introduction to Statistical Inference Stochastic Processes: An Introduction to Poisson Processes and Markov Chains The Analysis of DNA Sequence Patterns: One sequence The Analysis of DNA Sequences: Multiple sequences Stochastic Processes: Random Walks Statistics: Classical Estimation and Hypothesis Testing BLAST Stochastic Processes: Markov Chains Hidden Markov Models Computationally intensive methods Evolutionary models Phylogenetica tree estimation
An Introduction to Probability Theory: One Random Variable * An Introduction to Probability Theory: Many Random Variables * Statistics: An Introduction to Statistical Inference * Stochastic Processes: An Introduction to Poisson Processes and Markov Chains * The Analysis of DNA Sequence Patterns: One sequence * The Analysis of DNA Sequences: Multiple sequences * Stochastic Processes: Random Walks * Statistics: Classical Estimation and Hypothesis Testing * BLAST * Stochastic Processes: Markov Chains * Hidden Markov Models * Computationally intensive methods * Evolutionary models * Phylogenetica tree estimation
Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.
This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.
The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.
The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematica
This book provides an introductory account of probability theory,
statistics and stochastic process theory appropriate to computational
biology and bioinformatics. These topics are important in the analysis
of human genome data.