Concha Alborg

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

Microeconomic Modeling in Urban Science proposes an interdisciplinary framework for the analysis of urban systems. It portrays agents as rational beings modeled under the framework of random utility b

This book covers virtually all of the engineering science and technological aspects of separating water from particulate solids in the mining industry. It starts with an introduction to the field of m

本書介紹葡萄酒歷史、各式各樣的葡萄、釀製方式、飲用的餐桌禮儀等等。作者以最精煉的內容，從選購一瓶葡萄酒開始，到選擇最適合的酒杯，再灌輸正確用舌頭的不同部份領略葡萄酒真味的方法。