The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. ´Big data´, ´data science´, and ´machine learning´ have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
This textbook provides a straightforward introduction to the statistical analysis of language, a useful tool for linguists in understanding the quantitative structure of their data. Designed for those with a non-mathematical background, it clearly introduces the principles and methods of statistical analysis, using´R´, the leading computational statistics programme. The reader is guided step-by-step through the analysis of a range of data sets, aided by over 40 exercises with model answers. This book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.
If you know how to program with Python and also know a little about probability, you?re ready to tackle Bayesian statistics. With this book, you´ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you?ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book?s computational approach helps you get a solid start. * Use your existing programming skills to learn and understand Bayesian statistics * Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing * Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey * Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
This volume contains the papers from BIOWIRE 2007, the first in a series of wo- shops on the bio-inspired design of networks, and additional papers contributed from the research area of bio-inspired computing and communication. The workshop took place at the University of Cambridge during April 2-5, 2007 with sponsorship from the US/UK International Technology Alliance in Network and Information Sciences. Its objective was to present, discuss and explore the recent developments in the field of bio-inspired design of networks, with particular regard to wireless networks and the self-organizing properties of biological networks. The workshop was organized by Jon Crowcroft (University of Cambridge), Don Towsley (University of Massachusetts), Dinesh Verma (IBM T. J. Watson Research Center), Vasilis Pappas (IBM T. J. Watson Research Center), Ananthram Swami (ARL), Tom McCutcheon (DSTL) and Pietro Liò (University of Cambridge). The program for BIOWIRE 2007 included 54 speakers covering a diverse range of topics, categorized as follows: 1. Self-organized communication networks in insects 2. Neuronal communications 3. Bio-computing 4. Epidemiology 5. Network theory 6. Wireless and sensorial networks 7. Brain: models of sensorial integration The BIOWIRE workshop focuses on achieving a common ground for knowledge sharing among scientists with expertise in investigating the application domain (e. g. , biological, wireless, data communication and transportation networks) and scientists with relevant expertise in the methodology domain (e. g. , mathematics and statistical physics of networks).
This volume investigates the construction of group identity in Late La Tène South-East Europe using an innovative statistical modelling method. Death and burial theory underlies the potential of mortuary practices for identity research. The sample used for this volumes´s research consists of 370 graves, organized in a specially crated database that records funerary ritual; and grave-good information. In the case of grave-goods, this involved found hierarchically organized categorical variables, which serve to describe each item by combining functional and typological features. The volume also aims to show the compatibility of archaeological theory and statistical modelling. The discussions from archaeological theory rarely find methodological implementations through statistical methods. In this volume, theoretical issues form an integrative part of data preparation, method development and result interpretation.
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA package, available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page.
Statistical Gabor Graph Based Techniques for the Detection, Recognition, Classification, and Visualization of Human Faces:Berichte aus der Informatik. 1., Aufl. Manuel Günther